diff --git a/src/LLM/GenCode.py b/src/LLM/GenCode.py index f53528c..c870ef5 100644 --- a/src/LLM/GenCode.py +++ b/src/LLM/GenCode.py @@ -80,7 +80,7 @@ def get_runnable_function(self, error: str = None) -> Callable: action = env.envs[0].action_space.sample() obs, _, dones, infos = env.step([action]) infos[0]["terminated"] = False - is_success, is_failure = self.success_func(obs[0], infos[0]) + is_success, is_failure = self.success_func(env.envs[0], infos[0]) self.test_reward_function( reward_func, observations=obs[0], is_success=0, is_failure=0 diff --git a/src/PolicyTrainer/CustomRewardWrapper.py b/src/PolicyTrainer/CustomRewardWrapper.py index de987f8..080f15e 100644 --- a/src/PolicyTrainer/CustomRewardWrapper.py +++ b/src/PolicyTrainer/CustomRewardWrapper.py @@ -34,7 +34,7 @@ def step(self, action): is_success = 0 is_failure = 0 if terminated or truncated: - is_success, is_failure = self.success_func(observation, info) + is_success, is_failure = self.success_func(self.env, info) reward = self.llm_reward_function(observation, is_success, is_failure) else: reward = original_reward diff --git a/src/PolicyTrainer/PolicyTrainer.py b/src/PolicyTrainer/PolicyTrainer.py index 4fc1fc4..d1d5b75 100644 --- a/src/PolicyTrainer/PolicyTrainer.py +++ b/src/PolicyTrainer/PolicyTrainer.py @@ -180,7 +180,10 @@ def test_policy( obs, reward, term, trunc, info = env.step(actions) episode_rewards += reward done = trunc or term + if done: + info["TimeLimit.truncated"] = trunc + info["terminated"] = term is_success, _ = self.success_func(env, info) if is_success: nb_success += 1 diff --git a/src/VIRAL.py b/src/VIRAL.py index e163fff..7ad4999 100644 --- a/src/VIRAL.py +++ b/src/VIRAL.py @@ -2,10 +2,10 @@ from logging import getLogger from Environments import EnvType -from LLM.GenCode import GenCode from LLM.OllamaChat import OllamaChat -from PolicyTrainer.PolicyTrainer import PolicyTrainer from State.State import State +from LLM.GenCode import GenCode +from PolicyTrainer.PolicyTrainer import PolicyTrainer class VIRAL: diff --git a/src/log/CartPole-v1_log.csv b/src/log/CartPole-v1_log.csv deleted file mode 100644 index fb8dc03..0000000 --- a/src/log/CartPole-v1_log.csv +++ /dev/null @@ -1,2242 +0,0 @@ -env;llm;reward_function;rewards;mean_reward;std_reward;SR 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - terminated (bool): episode is terminated due a failure - truncated (bool): episode is truncated due a success - - Returns: - float: The reward for the current step - """""" - x, _, theta, _ = observations - if terminated or truncated: - return -1.0 - else: - return 2.0 - 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - terminated (bool): episode is terminated due a failure - truncated (bool): episode is truncated due a success - - Returns: - float: The reward for the current step - """""" - x, _, theta, _ = observations - if terminated or truncated: - return -1.0 - else: - return 1.0 - 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-CartPole-v1;qwen2.5-coder;"def reward_function(observations, terminated, truncated): - # Your reward calculation logic here - return 0.0271 # Placeholder return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - terminated (bool): episode is terminated due a failure - truncated (bool): episode is truncated due a success - - Returns: - float: The reward for the current step - """""" - if terminated or truncated: - return -1.0 # Negative reward for failure or truncation - else: - return 1.0 # Positive reward for continuing the 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - terminated (bool): episode is terminated due a failure - truncated (bool): episode is truncated due a success - - Returns: - float: The reward for the current step - """""" - if terminated or truncated: - return -1.0 # Negative reward for failure or truncation - else: - x, _, theta, _ = observations - return np.exp(-0.25 * (x**2 + theta**2)) # Reward based on cart and pole 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations:np.ndarray, terminated: bool, truncated: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - terminated (bool): episode is terminated due a failure - truncated (bool): episode is truncated due a success - - Returns: - float: The reward for the current step - """""" - x, _, theta, _ = observations - if terminated or truncated: - return -1.0 - else: - # Reward based on how close to the center of the track and stable the pole are - return np.cos(theta) * (np.abs(x) < 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations:np.ndarray, terminated: bool, truncated: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - terminated (bool): episode is terminated due a failure - truncated (bool): episode is truncated due a success - - Returns: - float: The reward for the current step - """""" - x, _, theta, _ = observations - if terminated or truncated: - return -1.0 - else: - # Reward based on how close to the center of the track and stable the pole are - return np.cos(theta) * (np.abs(x) < 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations:np.ndarray, terminated: bool, truncated: bool) -> float: - x, x_dot, theta, theta_dot = observations - if terminated or truncated: - return -1.0 - else: - return 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations:np.ndarray, terminated: bool, truncated: bool) -> float: - x, x_dot, theta, theta_dot = observations - if terminated or truncated: - return -1.0 - else: - return 1.0 - 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: - x = observations[0] - theta = observations[2] - - if terminated or truncated: - return -1.0 - - # Positive reward based on cart position and pole angle - reward = 1.0 - abs(x) / 2.4 - abs(theta) / 0.2094 - - # Penalize for moving cart too far left or right - if abs(x) > 1.5: - reward -= 0.5 - - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - terminated (bool): episode is terminated due a failure - truncated (bool): episode is truncated due a success - - Returns: - float: The reward for the current step - """""" - x, x_dot, theta, theta_dot = observations - return 1.0 - 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - terminated (bool): episode is terminated due a failure - truncated (bool): episode is truncated due a success - - Returns: - float: The reward for the current step - """""" - if terminated or truncated: - return -1.0 - else: - x, x_dot, theta, theta_dot = observations - return 1.0 - 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - terminated (bool): episode is terminated due a failure - truncated (bool): episode is truncated due to a success - - Returns: - float: The reward for the current step - """""" - x, x_dot, theta, theta_dot = observations - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - terminated (bool): episode is terminated due a failure - truncated (bool): episode is truncated due to a success - - Returns: - float: The reward for the current step - """""" - x, x_dot, theta, theta_dot = observations - if terminated or truncated: - return -1.0 - else: - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - terminated (bool): episode is terminated due a failure - truncated (bool): episode is truncated due a success - - Returns: - float: The reward for the current step - """""" - x, _, theta, _ = observations - if terminated or truncated: - return -1.0 - else: - return 1.0 - 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - terminated (bool): episode is terminated due a failure - truncated (bool): episode is truncated due to a success - - Returns: - float: The reward for the current step - """""" - x, _, theta, _ = observations - if terminated or truncated: - return -1.0 - else: - return 1.0 - 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: - # Extract observations - x, x_dot, theta, theta_dot = observations - - # Define the reward components - x_pos_reward = 1.0 - abs(x) / 2.4 # Reward based on cart position - theta_reward = np.cos(theta) # Reward based on pole angle (cosine gives higher reward for stable position) - - # Combine rewards - reward = x_pos_reward + theta_reward - - # Penalize if terminated or truncated - if terminated or truncated: - reward -= 10.0 - - return 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: - # Extract observations - x, x_dot, theta, theta_dot = observations - - # Define the reward components - x_pos_reward = 1.0 - abs(x) / 2.4 # Reward based on cart position - theta_reward = 1.0 - abs(theta) / (np.pi / 12) # Reward based on pole angle - - # Combine rewards - reward = x_pos_reward + theta_reward - - # Penalize if terminated or truncated - if terminated or truncated: - reward -= 10.0 - - return 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations:np.ndarray, is_success:bool) -> float: - x, x_dot, theta, theta_dot = observations - if is_success: - return 1.0 - else: - done = abs(x) > 2.4 or abs(theta) > 0.2095 # 2.4 is the horizontal limit and 0.2095 radians (12 degrees) is the angular limit - if done: - return -1.0 - else: - return 1.0 - (abs(x)**2 + 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations:np.ndarray, is_success:bool) -> float: - x, x_dot, theta, theta_dot = observations - if is_success: - return 1.0 - else: - done = abs(x) > 2.4 or abs(theta) > 0.2095 # 2.4 is the horizontal limit and 0.2095 radians (12 degrees) is the angular limit - if done: - return -1.0 - else: - return 1.0 - (abs(x)**2 + 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations: np.ndarray, is_success: bool) -> float: - x, x_dot, theta, theta_dot = observations - - if is_success: - return 1.0 - - # Penalize large positions and angles to discourage instability - position_penalty = abs(x) - angle_penalty = abs(theta) - - # Combine penalties with a decay factor for smoothness - total_penalty = 0.1 * (position_penalty + angle_penalty) - - return 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations: np.ndarray, is_success: bool) -> float: - x, x_dot, theta, theta_dot = observations - - if is_success: - return 1.0 - - # Penalize large positions and angles to discourage instability - position_penalty = abs(x) - angle_penalty = abs(theta) - - # Penalize high velocity and angular velocity to discourage rapid movement - velocity_penalty = abs(x_dot) + abs(theta_dot) - - # Combine penalties with decay factors for smoothness - total_penalty = 0.1 * (position_penalty + angle_penalty + 0.2 * velocity_penalty) - - return 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations:np.ndarray, is_success:bool) -> float: - x, x_dot, theta, theta_dot = observations - r_pos = 1.0 - abs(x) - r_vel = 0.9 - abs(x_dot) - r_angle = 0.8 - abs(theta) - r_ang_vel = 0.7 - abs(theta_dot) - - total_reward = r_pos + r_vel + r_angle + r_ang_vel - - if is_success: - total_reward += 100.0 - - return 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations:np.ndarray, is_success:bool) -> float: - x, x_dot, theta, theta_dot = observations - - if abs(x) > 0.5 or abs(theta) > 0.2: - return -1.0 - - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool) -> float: - x, x_dot, theta, theta_dot = observations[:4] - if is_success: - return 1.0 - else: - return -1.0 + abs(x) * 0.25 - abs(theta) * 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations:np.ndarray, is_success:bool) -> float: - x, theta = observations[:2] - return -abs(x) * 0.1 + (180/np.pi-abs(theta)) if is_success else -abs(x) * 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations:np.ndarray, is_success:bool) -> float: - x, theta = observations[:2] - return -abs(x) + (180/np.pi-abs(theta)) * 5 if is_success else -abs(x) + 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - x, x_dot, theta, theta_dot = observations - if is_success: - return 1.0 - else: - return -1.0 * (x**2 + x_dot**2 + 0.1 * theta**2 + 0.01 * 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - return 0 if not is_success else 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - return observations[0] ** 2 if not is_success else 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - position = observations[0] - velocity = observations[1] - angle = observations[2] - angular_velocity = observations[3] - - # Penalize large positions and angles to discourage instability - pos_cost = 0.5 * abs(position) - angle_cost = 0.1 * abs(angle) - - # Reward small velocities and angular velocities to encourage stability - vel_reward = 0.1 * abs(velocity) ** 2 - ang_vel_reward = 0.1 * abs(angular_velocity) ** 2 - - # Total reward is a balance between stability and movement - total_reward = -pos_cost - angle_cost + vel_reward + ang_vel_reward - - if is_success: - return 1.0 # Additional reward for successful completion - else: - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - position = observations[0] - velocity = observations[1] - angle = observations[2] - angular_velocity = observations[3] - - # Penalize large positions and angles to discourage instability - pos_cost = 0.5 * abs(position) - angle_cost = 0.1 * abs(angle) - - # Reward small velocities and angular velocities to encourage stability - vel_reward = 0.1 * abs(velocity) ** 2 - ang_vel_reward = 0.1 * abs(angular_velocity) ** 2 - - # Total reward is a balance between stability and movement - total_reward = -pos_cost - angle_cost + vel_reward + ang_vel_reward - - if is_success: - return 1.0 # Additional reward for successful completion - else: - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - x = observations[0] - theta = observations[2] - - # Define success conditions - is_stable = abs(x) <= 0.5 and abs(theta) <= 0.2 - - # Assign rewards based on conditions - if is_success or is_stable: - return 1.0 - else: - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - x = observations[0] - theta = observations[2] - - # Define success conditions - is_stable = abs(x) <= 0.5 and abs(theta) <= 0.2 - - # Assign rewards based on conditions - if is_success or is_stable: - return 1.0 - else: - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - x, _, theta, _ = observations - r = np.cos(theta) * 0.5 + 0.5 - return r if not is_success else 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - x, _, theta, _ = observations - r = np.cos(theta) * 0.5 + 0.5 - return r if not is_success else 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations:np.ndarray, is_success:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - x, _, theta, _ = observations - reward = 0.0 - if is_success: - reward += 100.0 - else: - reward -= abs(x) * 0.5 + abs(theta) * 0.5 - return 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations:np.ndarray, is_success:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - x, _, theta, _ = observations - reward = 0.0 - if is_success: - reward += 100.0 - else: - reward -= abs(x) * 0.5 + abs(theta) ** 2 - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - x, _, theta, _ = observations - # Reward function logic - if is_success: - return 1.0 - elif abs(x) > 2 or abs(theta) >= np.pi / 6: - return -1.0 - else: - return 0.5 - -# 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - x, _, theta, _ = observations - # Reward function logic - if is_success: - return 1.0 - elif abs(x) > 2 or abs(theta) >= np.pi / 6: - return -1.0 - else: - return 0.5 - -# 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - if is_success: - return 1.0 - else: - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - if is_success: - return 2.0 - else: - angle = observations[2] - angular_velocity = observations[3] - return -angle**2 - 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - cart_position, cart_velocity, pole_angle, pole_angular_velocity = observations - - # Reward for maintaining balance - balance_reward = 1.0 - abs(pole_angle) / np.pi # Pole angle between -pi/2 and pi/2 - - # Penalty for cart movement - movement_penalty = abs(cart_position) * 0.1 + abs(cart_velocity) * 0.1 - - # Reward for success - if is_success: - success_reward = 100.0 - else: - success_reward = 0.0 - - return balance_reward - movement_penalty + 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - cart_position, cart_velocity, pole_angle, pole_angular_velocity = observations - - # Reward for maintaining balance - balance_reward = 1.0 - abs(pole_angle) / np.pi # Pole angle between -pi/2 and pi/2 - - # Penalty for cart movement - movement_penalty = abs(cart_position) * 0.1 + abs(cart_velocity) * 0.1 - - # Reward for success - if is_success: - success_reward = 100.0 - else: - success_reward = 0.0 - - return balance_reward - movement_penalty + 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - theta = observations[2] # Angular position of the pole - angular_velocity = observations[3] # Angular velocity of the pole - - if is_success: - return 1.0 # Goal achieved - - # Reward based on how close to vertical and not swinging too fast - reward = np.cos(theta) - abs(angular_velocity) * 0.05 - return max(reward, -2.0) # Cap the minimum reward at 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - theta = observations[2] # Angular position of the pole - angular_velocity = observations[3] # Angular velocity of the pole - - if is_success: - return 1.0 # Goal achieved - - # Reward based on how close to vertical and not swinging too fast - reward = np.cos(theta) - abs(angular_velocity) * 0.05 - return max(reward, -2.0) # Cap the minimum reward at 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - cart_position = observations[0] - cart_velocity = observations[1] - pole_angle = observations[2] - pole_angular_velocity = observations[3] - - # Define the threshold values for successful balance - position_threshold = 0.5 - angle_threshold = 0.2 - - if is_success: - return 1.0 # Maximum reward for successfully balancing the pole - - # Reward based on proximity to the target state - cart_position_reward = max(0, 1 - abs(cart_position) / position_threshold) - pole_angle_reward = max(0, 1 - abs(pole_angle) / angle_threshold) - - # Penalize for movement and rotation - velocity_penalty = abs(cart_velocity) * 0.01 - angular_velocity_penalty = abs(pole_angular_velocity) * 0.01 - - return cart_position_reward + pole_angle_reward - velocity_penalty - 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - cart_position = observations[0] - cart_velocity = observations[1] - pole_angle = observations[2] - pole_angular_velocity = observations[3] - - # Define the threshold values for successful balance - position_threshold = 0.5 - angle_threshold = 0.2 - - if is_success: - return 1.0 # Maximum reward for successfully balancing the pole - - # Reward based on proximity to the target state - cart_position_reward = max(0, 1 - abs(cart_position) / position_threshold) - pole_angle_reward = max(0, 1 - abs(pole_angle) / angle_threshold) - - # Penalize for movement and rotation - velocity_penalty = abs(cart_velocity) * 0.01 - angular_velocity_penalty = abs(pole_angular_velocity) * 0.01 - - return cart_position_reward + pole_angle_reward - velocity_penalty - 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0.5 -CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations:np.ndarray, is_success:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - cart_position = observations[0] - cart_velocity = observations[1] - pole_angle = observations[2] - pole_angular_velocity = observations[3] - - # Reward for successfully balancing the pole - if is_success: - return 1.0 - - # Reward based on the angle of the pole (smaller angles are better) - angle_reward = np.cos(pole_angle) - - # Penalize large cart velocities and angular velocities to encourage stability - velocity_penalty = -abs(cart_velocity) * 0.01 - angular_penalty = -abs(pole_angular_velocity) * 0.01 - - return angle_reward + velocity_penalty + 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations:np.ndarray, is_success:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - cart_position = observations[0] - cart_velocity = observations[1] - pole_angle = observations[2] - pole_angular_velocity = observations[3] - - # Reward for successfully balancing the pole - if is_success: - return 1.0 - - # Reward based on the angle of the pole (smaller angles are better) - angle_reward = np.cos(pole_angle) - - # Penalize large cart velocities and angular velocities to encourage stability - velocity_penalty = -abs(cart_velocity) * 0.01 - angular_penalty = -abs(pole_angular_velocity) * 0.01 - - return angle_reward + velocity_penalty + 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - cart_position, cart_velocity, pole_angle, pole_angular_velocity = observations - - # Reward for maintaining balance - balance_reward = np.cos(pole_angle) - - # Penalty for cart position too far from center - position_penalty = -np.abs(cart_position) * 0.1 - - # Penalty for high angular velocity of the pole - angular_velocity_penalty = -np.abs(pole_angular_velocity) * 0.05 - - # Total reward - total_reward = balance_reward + position_penalty + angular_velocity_penalty - - # Additional reward if successful - if is_success: - total_reward += 10 - - return max(total_reward, 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - cart_position, cart_velocity, pole_angle, pole_angular_velocity = observations - - # Reward for maintaining balance - balance_reward = np.cos(pole_angle) - - # Penalty for cart position too far from center - position_penalty = -np.abs(cart_position) * 0.1 - - # Penalty for high angular velocity of the pole - angular_velocity_penalty = -np.abs(pole_angular_velocity) * 0.05 - - # Total reward - total_reward = balance_reward + position_penalty + angular_velocity_penalty - - # Additional reward if successful - if is_success: - total_reward += 10 - - return max(total_reward, 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - cart_position = observations[0] - cart_velocity = observations[1] - pole_angle = observations[2] - pole_angular_velocity = observations[3] - - # Define a reward based on the state and success flag - if is_success: - return 1.0 # Maximum reward for achieving the goal - - # Penalize the agent for being too far from the center or moving too fast - cart_position_reward = -abs(cart_position) - cart_velocity_reward = -abs(cart_velocity) * 0.1 - pole_angle_reward = -abs(pole_angle) * 0.5 - pole_angular_velocity_reward = -abs(pole_angular_velocity) - - # Combine the rewards - total_reward = cart_position_reward + cart_velocity_reward + pole_angle_reward + pole_angular_velocity_reward - - return max(total_reward, 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - cart_position = observations[0] - cart_velocity = observations[1] - pole_angle = observations[2] - pole_angular_velocity = observations[3] - - # Define a reward based on the state and success flag - if is_success: - return 1.0 # Maximum reward for achieving the goal - - # Penalize the agent for being too far from the center or moving too fast - cart_position_reward = -abs(cart_position) * 0.5 - cart_velocity_reward = -abs(cart_velocity) - pole_angle_reward = -abs(pole_angle) * 1.0 - pole_angular_velocity_reward = -abs(pole_angular_velocity) * 2.0 - - # Combine the rewards - total_reward = cart_position_reward + cart_velocity_reward + pole_angle_reward + pole_angular_velocity_reward - - return max(total_reward, 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - cart_position = observations[0] - cart_velocity = observations[1] - pole_angle = observations[2] - pole_angular_velocity = observations[3] - - # Define a reward based on the state and success flag - if is_success: - return 1.0 # Maximum reward for achieving the goal - - # Penalize the agent for being too far from the center or moving too fast - cart_position_reward = -abs(cart_position) * 0.25 - cart_velocity_reward = -abs(cart_velocity) * 0.5 - pole_angle_reward = -abs(pole_angle) * 1.5 - pole_angular_velocity_reward = -abs(pole_angular_velocity) * 3.0 - - # Combine the rewards - total_reward = cart_position_reward + cart_velocity_reward + pole_angle_reward + pole_angular_velocity_reward - - return max(total_reward, 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations: np.ndarray, is_success: bool) -> float: - angle = observations[2] - - # Define thresholds to determine how far from vertical the pole should be considered successful. - success_angle_threshold = 0.5 # radians - - def calculate_distance_from_vertical(angle): - return abs(np.sin(angle)) - - distance = calculate_distance_from_vertical(angle) - - # Calculate a smooth penalty based on distance from vertical and add an additional bonus if successfully balanced. - penalization_factor = (1 / (success_angle_threshold * 2)) ** 3 - intermediate_reward = -penalization_factor * max(distance- success_angle_threshold, 0)**6 - final_reward = +intermediate_reward - - return final_reward if is_success else 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations: np.ndarray, is_success: bool) -> float: - angle = observations[2] - velocity_of_angle = observations[3] - - # Define thresholds to determine how far from vertical the pole should be considered successful and for balance stability. - success_angle_threshold = 0.15 # radians - stable_velocity_bound = 2 # rad/s - - def calculate_distance_from_vertical(angle): - return abs(np.sin(angle)) - - distance = calculate_distance_from_vertical(angle) - - # Calculate a smooth penalty based on distance from vertical and add an additional bonus if successfully balanced. - penalization_factor = (1 / (success_angle_threshold * 2)) ** 4 - intermediate_reward = -penalization_factor * max(distance- success_angle_threshold, 0)**6 - - # Penalize the cart's movement speed to prevent excessive oscillations around balance point for better stability control. - velocity_penalty = min(abs(velocity_of_angle), stable_velocity_bound) / float(stable_velocity_bound) - - final_reward = intermediate_reward + (is_success*(-intermediate_reward)) - - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - cart_pos, cart_vel, pole_angle, pole_ang_vel = observations - - # Reward for successfully balancing the pole - if is_success: - return 1.0 - - # Penalize if pole angle exceeds a certain threshold (e.g., ±20 degrees) - angle_threshold = np.deg2rad(20) - if abs(pole_angle) > angle_threshold: - return -1.0 - - # Reward for small cart position and velocity - pos_reward = 0.5 * (1 - abs(cart_pos)) - vel_reward = 0.5 * (1 - abs(cart_vel)) - - # Penalize large angular velocity - ang_vel_penalty = 0.3 * abs(pole_ang_vel) - - total_reward = pos_reward + vel_reward - ang_vel_penalty - - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - cart_pos, cart_vel, pole_angle, pole_ang_vel = observations - - # Reward for successfully balancing the pole - if is_success: - return 1.0 - - # Penalize if pole angle exceeds a certain threshold (e.g., ±15 degrees) - angle_threshold = np.deg2rad(15) - if abs(pole_angle) > angle_threshold: - return -1.0 - - # Reward for small cart position and velocity - pos_reward = 0.5 * (1 - abs(cart_pos)) - vel_reward = 0.5 * (1 - abs(cart_vel)) - - # Penalize large angular velocity - ang_vel_penalty = 0.2 * abs(pole_ang_vel) - - total_reward = pos_reward + vel_reward - ang_vel_penalty - - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - cart_pos, cart_vel, pole_angle, pole_ang_vel = observations - - # Reward for successfully balancing the pole - if is_success: - return 1.0 - - # Penalize if pole angle exceeds a certain threshold (e.g., ±15 degrees) - angle_threshold = np.deg2rad(15) - if abs(pole_angle) > angle_threshold: - return -1.0 - - # Penalize large angular velocity - vel_penalty = 0.1 * abs(pole_ang_vel) - - # Reward for small cart position and velocity - pos_reward = 0.1 * (1 - abs(cart_pos)) - vel_reward = 0.1 * (1 - abs(cart_vel)) - - total_reward = pos_reward + vel_reward - vel_penalty - - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - return 0 if is_success else 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise. - - Returns: - float: The reward for the current step - """""" - return 2 if is_success else 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode is finish without success, False otherwise - - Returns: - float: The reward for the current step - """""" - cart_position = observations[0] - cart_velocity = observations[1] - pole_angle = observations[2] - pole_angular_velocity = observations[3] - - if is_success: - return 1.0 - elif is_failure: - return -1.0 - else: - # Reward based on proximity to the goal and smoothness of control - distance_to_center = abs(cart_position) - angle_error = abs(pole_angle) - - cart_reward = np.exp(-distance_to_center ** 2 / (2 * 0.25 ** 2)) - pole_reward = np.exp(-angle_error ** 2 / (2 * np.pi / 8) ** 2) - - return cart_reward * 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode is finish without success, False otherwise - - Returns: - float: The reward for the current step - """""" - x, dx, theta, dtheta = observations - - # Reward for maintaining balance - balance_reward = np.exp(-0.1 * (x**2 + 0.5 * theta**2)) - - # Penalty for excessive movement or angular velocity - penalty = -np.abs(dx) / 2.4 - np.abs(dtheta) / (np.pi / 18) - - # Success reward - success_reward = 1 if is_success else 0 - - # Failure penalty - failure_penalty = -1 if is_failure else 0 - - return balance_reward + penalty + success_reward + 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode is finish without success, False otherwise - - Returns: - float: The reward for the current step - """""" - x, dx, theta, dtheta = observations - - # Reward for maintaining balance - balance_reward = np.exp(-0.1 * (x**2 + 0.5 * theta**2)) - - # Penalty for excessive movement or angular velocity - penalty = -np.abs(dx) / 2.4 - np.abs(dtheta) / (np.pi / 18) - - # Success reward - success_reward = 1 if is_success else 0 - - # Failure penalty - failure_penalty = -1 if is_failure else 0 - - return balance_reward + penalty + success_reward + 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - cart_position, cart_velocity, pole_angle, pole_angular_velocity = observations - - if is_success: - return 1.0 - elif is_failure: - return -1.0 - else: - # Encourage actions that keep the cart close to center and the pole upright - reward = -abs(cart_position) - abs(pole_angle) - return max(reward, 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - cart_position, cart_velocity, pole_angle, pole_angular_velocity = observations - - if is_success: - return 1.0 - elif is_failure: - return -1.0 - else: - # Encourage actions that keep the cart close to center and the pole upright - reward = -abs(cart_position) - abs(pole_angle) - return max(reward, 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - cart_position, cart_velocity, pole_angle, pole_angular_velocity = observations - - if is_success: - return 1.0 - elif is_failure: - return -1.0 - else: - # Encourage actions that keep the cart close to center and the pole upright - reward = -abs(cart_position) * 0.5 - abs(pole_angle) - return max(reward, 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - cart_position, cart_velocity, pole_angle, pole_angular_velocity = observations - - if is_success: - return 1.0 - elif is_failure: - return -1.0 - else: - # Encourage actions that keep the cart close to center and the pole upright - reward = -abs(cart_position) * 0.5 - abs(pole_angle) ** 2 - return max(reward, 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - theta = observations[2] - reward = -np.abs(theta) # Penalize based on how close to vertical the pole is - if is_success: - reward += 100.0 # Large positive reward for success - elif is_failure: - reward -= 50.0 # Negative reward for failure - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - cart_pos, cart_vel, pole_angle, pole_ang_vel = observations - - # Success奖励 - if is_success: - return 10.0 - - # Failure惩罚 - if is_failure: - return -5.0 - - # 保持杆直立的奖励（角度越小，奖励越高） - angle_reward = np.exp(-abs(pole_angle) / (np.pi / 6)) - - # 防止过快或过慢移动的惩罚 - vel_penalty = abs(cart_vel) - - return angle_reward - 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - x, dx, theta, dtheta = observations - - # Define rewards based on success and failure conditions - if is_success: - return 100.0 - elif is_failure: - return -100.0 - - # Penalize poor performance to encourage stability - reward = np.clip(-abs(theta), -2, 0) - - # Reward for keeping the cart stationary and upright - if abs(dx) < 0.05 and abs(dtheta) < 0.01: - reward += 1 - - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - if is_success: - return 1.0 - elif is_failure: - return -1.0 - else: - x, dx, theta, dtheta = observations - reward = np.exp(-0.5 * (x**2 + dx**2 + theta**2 + dtheta**2)) - return reward";0.003647727787825481,0.008510238890186208,0.020282360001605285,0.004350000376004425,0.010609233861143312,0.005048281730663061,0.003557322530655414,0.01665382610086771,0.0027857260400502536,0.01164811172266512,0.005212211397734088,0.011148173976644282,0.01651240868148184,0.0023440565554821145,0.0096124752642598,0.0026656828135779172,0.01039750587571795,0.00938427114273474,0.00374730576236297,0.0015579700949187327,0.007877393674889212,0.01877821624455653,0.009658312753319317,0.01344246850451516,0.005648708775493205,0.0029520406226100524,0.018292809928462148,0.020200527431865054,0.005660838267690444,0.006845044142282815,0.0022959113100487977,0.006263760988327051,0.005187694601323502,0.00209217440238726,0.030794125903041335,0.010236580712151964,0.008316116601561096,0.0029790010338705437,0.004222449104984835,0.006596838539629124,0.007911126778883148,0.008941320394038874,0.001840474341501589,0.003833772077581611,0.020970728608434207,0.0044041951516095735,0.013351524742891268,0.002621818364601195,0.0010479607334044452,0.005824769348944441,0.005303393798696155,0.0025551150078251574,0.0026881631075476187,0.0018639368645082717,0.012936312769608833,0.006392194573126446,0.0015736268053092135,0.0047053028064617435,0.0028000524744641476,0.0023224068248098393,0.0038326468374971718,0.0028104130247669297,0.006335950686777807,0.0028194487189314096,0.007899938868730402,0.007014220655194418,0.0043964193849975325,0.00917851866182241,0.004824272312719695,0.004609861403253721,0.0067075140884468865,0.004807729846413704,0.0018938240333087933,0.012601997273851709,0.00351042561037238,0.005784253609864709,0.005866758544642495,0.006284816177096665,0.005813942888165841,0.007610879324152721,0.0034853099361885054,0.010609047619706937,0.001261779809092252,0.004299046293996039,0.0028646849810228326,0.0029958695519048764,0.003045544006504912,0.0020364651247593473,0.003323359813006216,0.003887273757267764,0.002945790727118634,0.008514099925551586,0.036945099037300325,0.001129075118937831,0.011684583515810603,0.003060026596868753,0.005768776439693332,0.004769236623182249,0.012302586505759182,0.006855596417570275,0.005552583526881564,0.0018529391081254958,0.009071023064481692,0.005184615601961769,0.004903901868947602,0.003102012504770868,0.011260159913593297,0.009126856217718084,0.011951304382126878,0.007243791180763784,0.0038182783616489534,0.00630212495469668,0.02130721966317184,0.01790218942691183,0.01153926419870905,0.009274435248454732,0.011302940427216721,0.0025402993953184383,0.010431758105103207,0.003511112763313071,0.01642491413410546,0.004730597396327065,0.00410283926457466,0.0059427543541404875,0.002957125597442728,0.024102291912843382,0.00238070652132459,0.0045354078320701124,0.007743347852929798,0.0020189297099812513,0.007375099725959972,0.008396783548935045,0.0058277731884295615,0.004679619154761398,0.001481889423674668,0.002125329236107267,0.011746421099703563,0.0021826337933328315,0.0014745605909258534,0.0038474966452352417,0.003759963959784896,0.005396143383585904,0.008219087079851355,0.007616398241388576,0.003044704168464354,0.009290567522734982,0.007582427197520693,0.005534561487795372,0.0019579404725570963,0.007389794120732548,0.007739924568211586,0.004309857521798691,0.009024367405966864,0.013502704850577653,0.01853821678181098,0.009712592692171475,0.0027573004167863674,0.004394072756538551,0.010741871388374563,0.015167193934639034,0.00816590172579633,0.0039449922138140815,0.01168786818188341,0.009696581377195994,0.008354364254970766,0.001546178249673398,0.0030958025761001796,0.0020564923837694898,0.011211419668397636,0.004743715770323047,0.013422363493707398,0.006255736321923047,0.014012552986260153,0.027400525545793763,0.0031049050904099775,0.01291479520043688,0.012848110838219918,0.01673536834315142,0.010080844336652241,0.006304472042585662,0.005968219819662251,0.01417832684483467,0.0023148716703032704,0.0188114275204186,0.006126032596226948,0.0014388334422837953,0.004516216299560955,0.013219132594351743,0.015520196307764354,0.015433522318520361,0.0013547256495566628,0.0025635159976495673,0.010697939715456625,0.01388022735457036,0.011786591519709316,0.005773301746231807,0.0035191811976031974,0.01967168217434946,0.008092103764812454,0.005362029744131676,0.012528440147509711,0.007248699441948646,0.03309770508902734,0.02649364091835653,0.008077601067207505,0.017153307406772542,0.009430836684772867,0.0027038613317457833,0.006163207796444031,0.0069410607653110825,0.01420860229120885,0.012489306503085586,0.008569181117502869,0.019827330594129205,0.007257156475054499,0.01931140226132235,0.006679357145590054,0.007724577373658733,0.007219435904507328,0.011579630479466807,0.031157665741117876,0.007530385278593303,0.019834784113667594,0.037209145267262475,0.002200881563299643,0.031883303061910076,0.015232606881758901,0.0030658883565637418,0.011840808696116275,0.002747881764386203,0.007000074590127206,0.019042105930924678,0.013370046347495449,0.0068489219369726375,0.004513523580329166,0.00843601240304814,0.00914435129777252,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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - if is_success: - return 10.0 - elif is_failure: - return -10.0 - else: - cart_position, cart_velocity, pole_angle, pole_angular_velocity = observations - return np.cos(pole_angle) * (1 - abs(cart_position))";-0.00023294253991161537,0.0003392525675023479,0.011703996344616285,0.004736633321730253,0.006157517029779757,0.005210190380301307,0.020890831210730702,-0.00144320123880156,0.0083127296207583,0.008213162644292112,9.954277944783178e-05,0.01630298476016773,0.0003784674968947067,0.002616494253105286,0.004036468733100988,0.009385302527570527,-0.0008958387024577184,-0.0014287649110927593,0.014232603681842537,0.002198449875105853,0.002569994943286789,0.016247647219919448,0.0015387569740635005,0.018856135605158798,0.012600474056504996,0.0040329926511444986,0.002826032775254761,0.02083500151576652,-0.0008742609091239762,0.008498038267156614,0.003368058772565626,0.004131512391063557,-0.00027419258350681294,0.007786897635950458,0.0036790518244580544,-0.0014233801638680655,0.005716303988119921,0.008901443672064639,0.020631212331553034,0.00150984633192891,0.006560261458378373,0.011022920750315997,0.009090669410592705,0.004680954347057058,0.00982272894028714,0.014341073451289791,8.970552066263431e-05,0.0004353832328409875,0.004609481186803199,-0.0014375469419960398,0.001725565538247896,0.01041499222214002,0.0010685002995906125,-0.00027784313674465944,0.007235801547993979,0.01942412351640573,6.098562983096723e-05,0.0008965536481536913,0.009403704192769797,3.180250176462844e-05,0.0017929075129376995,0.0019581233046817516,0.0016636217287914292,-0.0008609695352607043,0.007253457714580089,0.016401673730539284,0.008433011809598186,0.006403413188860634,-0.001049321950988013,-0.0008321465747947802,0.0032767756880779757,0.003043707851470695,0.0025281060017675943,0.009801153562938331,0.004344267852908418,0.0003259993662010602,0.0018537150289254071,0.00374235062977346,-0.0014351239692885233,0.005762932200026987,0.007361744389538217,-0.0003370235426316698,0.014054519648119951,0.01628090046488447,0.005649892985348341,0.011549398657988649,0.006688173587828201,-0.00039311208250496503,0.00189425094454261,0.0008143059012975806,0.0033067436395584275,0.00020569331486380892,0.02700196478646188,0.0015606638245454854,0.00781115527694388,0.00850590854251416,0.0009115042461328056,-0.0009286143257854489,0.009827250140157813,0.00831008512081668,0.0005881289369239105,0.003516592931708632,0.01975453900571726,0.003991039295750018,0.01853847068077493,0.0021034802577893142,0.0015568933641168302,0.00687707458081191,0.005998958763293526,0.004519954374687808,-0.001116893481415707,0.029556436952198784,0.01174238288852769,0.001048448851201249,0.010956145454185112,0.00484688631107405,0.00223379688695415,0.02013552927408183,0.005406198457585872,0.0008814432234788306,0.00047041516308467,0.009284523655891592,0.0036193773682361924,0.001928139370531725,0.008726160099403524,0.0005544171380180949,0.006113242953594039,-0.001093623978260987,0.003988311202729622,0.000889801713638383,-0.00023411823251932623,0.003383316200631574,0.009470204995842987,0.00368584932816615,0.006924164208672697,0.0002960025715773758,0.010205053984575182,0.0039663682239805985,0.004613866310968496,0.005139540439037539,0.007071018998102397,0.02276773394313626,0.004081321419094307,-0.00021684486945402855,0.009461795732830786,0.0033495800560314065,0.0014939366622683198,0.0008193221922350802,0.0067358009395976776,0.009466520878999034,0.015585783760841862,0.0079312934359126,0.008747608024254724,0.010634634764980593,0.003343858855027667,0.0066797935768182215,0.002149828475626832,0.011038436651601067,0.002771073652585408,0.008165337479109901,0.003793277993951112,0.020999937908145007,-0.0011504450447667872,0.006821004216579132,0.00911429833796786,0.014945378262245207,0.004736334594485203,0.011406551764553284,0.00951194217633922,0.006175970471394649,0.004633563613135285,0.002284828733188021,0.0009892264815143555,0.022034642592737033,0.0014239368439662504,-0.000772673871899549,0.015638672049900326,0.0027843948731548856,0.0007732367592205686,0.01763632239205913,-0.0008341500210104618,0.00894885856583577,-0.0008345799919910188,0.002061320056915339,0.0022825493443236833,0.00581544519370244,0.00457819310398488,0.018311069815601832,0.02539300055391283,0.009552580678393692,0.001312313805318915,0.015794617856122083,0.0015450151555959492,0.005267348420952138,0.027269439630932476,0.03498674623323198,0.00734618102344141,0.010530468013379821,0.004783870807869105,0.015911464886073392,0.010703347532353915,0.002127642857652266,0.0025813722995459993,0.016460600123487056,0.0033149105978660773,0.008048620391979136,0.014039155582195841,0.01192305641207336,0.00726233705392278,0.02104111149510924,0.021682198338626762,0.014291620246781907,0.009010335049467673,0.0009962412380216691,0.013753312115766097,0.009537112539272351,0.002834456459838578,0.014757949087284328,0.0026286080015728982,0.012130616985940474,0.007627267638546749,0.00560065461765474,0.00481640122731036,0.0014867110088606186,0.01726929443515898,0.01461215420903042,0.02399870825396664,-0.0008312815778498007,0.021907036698670057,0.006322320877507772,0.0015861785582544526,0.007538135115172866,0.005089263866863568,0.019862653030674984,0.0067746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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - x, x_dot, theta, theta_dot = observations - if is_success: - return 1.0 - elif is_failure: - return -1.0 - else: - # Reward based on proximity to goal state - close_to_goal = (np.abs(theta) < np.pi/3 and np.abs(x_dot) < 0.2 and np.abs(theta_dot) < 0.5) - if close_to_goal: - return 0.5 - else: - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - theta = observations[2] - x_dot = observations[3] - - if is_success: - return 1.0 - elif is_failure: - return -1.0 - else: - # Reward based on angle and velocity to encourage stability - return np.exp(-0.5 * (theta**2 + x_dot**2))";0.004082466469219327,0.007790705682870362,0.014497053097774631,0.003772544142566085,0.013567578980497881,0.010347040716051225,0.0038181297379582375,0.004726839734055168,0.0038847660851100644,0.0012302541217649007,0.005878373085635604,0.0067782553249724955,0.007163557956832485,0.006266844741932815,0.0012479622532778603,0.006780579507045093,0.00346999008913791,0.00434471209906456,0.002660955127025589,0.0014304041263334764,0.003451517881585639,0.007703408575123079,0.004369180559257863,0.006038485099503444,0.002483108803926446,0.0037228973336997603,0.00892255854905354,0.0027122948895900697,0.006601978049243478,0.0014263014809144784,0.0059352455852862305,0.013683220478024562,0.0029595521065768427,0.007644201407020783,0.006515981356369459,0.002579237113401883,0.003781237557110597,0.002446505631090594,0.00760314965655112,0.009956436129673804,0.006248551415936758,0.004557726249323247,0.0031356835337814225,0.006825312625746297,0.005212733974925413,0.011488496914129582,0.0024432091716854714,0.007728169799901441,0.00332473975427223,0.005262137298985064,0.00781458936405411,0.03275867446368828,0.002544690061417887,0.0034403777722080016,0.0020096206145440968,0.00685254726838515,0.0027782589168801153,0.0021382316899424916,0.0027536731950502107,0.0018577924102431158,0.005999507553083004,0.010365877491537783,0.002049433978320567,0.004533413314725617,0.004164466745180239,0.0037780528370062543,0.00608892913862064,0.010994376245204922,0.006835072889788202,0.008029844468956394,0.004293178434421249,0.003246003992417478,0.004520597190852335,0.0024439925008841023,0.003996938338172582,0.018159360182247637,0.0027256722835950218,0.005972867066460719,0.003546382966840235,0.001980340889954565,0.004878449959008547,0.004752434058300144,0.002712668420500442,0.0021411550541074144,0.010675839588241686,0.0024301294067682923,0.0030621727441017734,0.004283986820936841,0.007775328764742642,0.004264847062812816,0.0022684117074000094,0.0020115434664870744,0.0024178139515942916,0.004087604033262919,0.004937042161351793,0.005552871176323413,0.0022833097764024556,0.004169605016105899,0.003126082396105884,0.005709268377782245,0.001246442295260748,0.0027779979071686466,0.0031716269519448887,0.005551086284678052,0.004930681197957446,0.0012003389880984173,0.0033704227551279832,0.007767114737419345,0.011139044449704873,0.0030848268313396717,0.002943293392021174,0.01271088090525417,0.01302401072227001,0.00422574434344709,0.011301793768433899,0.005164245385112799,0.012585408980591274,0.00915669944233672,0.0026816557615400903,0.011689573785349653,0.002831350589992374,0.0022051225550317024,0.009740226376212175,0.003958302732247202,0.010457841001906517,0.002566928917473926,0.00944977057762329,0.010613493617074456,0.010255882279439073,0.006769147550586468,0.010987105752523737,0.009133390707288715,0.0069353747925603,0.00436174650272207,0.0009474159843943224,0.002738724901355595,0.011494191994616808,0.007771324661569018,0.0035626314445894804,0.004827523682060005,0.004622497922368827,0.0018460899805090227,0.0021845609772033294,0.015166572817918722,0.007452572111281585,0.009660558161894983,0.012228253434742233,0.014282418404508546,0.0025176295021928914,0.004501285484386819,0.0028160137131272083,0.004093716246300503,0.005033915674557968,0.012838916151635545,0.004736217144999115,0.008756964107906093,0.004260465588483795,0.002920786370876008,0.0059322396053616885,0.012809404371728925,0.0065886342162679275,0.008394377716971707,0.0023854924067168042,0.0030522732329483666,0.0011257925036665165,0.009395624473056155,0.004584940285628538,0.008928864312171535,0.010218985017696249,0.0014893845007015797,0.011460557611053074,0.007077436506486721,0.001967858293237129,0.003651289228184932,0.002327474533069455,0.00288064413093917,0.001041452110491554,0.004496458507012073,0.0037571613385631946,0.0013687415918304945,0.00338543064619393,0.007277640522615682,0.001777809939240795,0.0031065591789849866,0.0019838540592048023,0.009699310241788377,0.003583583059745798,0.01343692048899537,0.00269270792324766,0.010872555588581063,0.007331468639891514,0.003990979556051567,0.008339365078858805,0.005989632961686384,0.0034229945381192037,0.012779626138397613,0.01292027195311765,0.02400712208908462,0.008152080495253294,0.002989974977763277,0.013064660457367233,0.008531877270299696,0.01885972909559539,0.013092247133045923,0.021227168588349745,0.016253130282552267,0.03945230776789985,0.006933151593640133,0.014615682865666151,0.0060564463784681475,0.013538126620881422,0.006422147124446264,0.010001065097236824,0.001631914345112952,0.006471557950111524,0.009839174885464014,0.015198691865608493,0.002485012630643506,0.016661667294122513,0.019851512642564354,0.015012137482522503,0.004837469475234787,0.0047151553312564,0.0070259635125213896,0.019262950483857212,0.0023651883763203883,0.013929399884514054,0.01222728289431993,0.01390192364978133,0.01000198396890788,0.02165530202405593,0.004980886951457702,0.009270655179050931,0.01156157854849327,0.0032785152644417507,0.004476101042101856,0.01445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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - angle = observations[2] - cart_position = observations[0] - - if is_success: - return 1.0 - elif is_failure: - return -1.0 - else: - # Reward based on the angle to keep the pole upright - return np.exp(-angle**2) + (0.5 * (cart_position >= 0))";0.005877339913783397,0.004840063519775857,0.0048399716959947586,0.00690670252042375,0.006577564386701257,0.009527025939687737,0.004131390139194681,0.00899334886123472,0.019707039175830054,0.010542611764916164,0.018717271489584287,0.0058533614646698005,0.008612860413851649,0.003963756909854971,0.004477310147241066,0.004488250677029582,0.0048238877695029455,0.006576315973135376,0.006918068450629037,0.0058937543478233476,0.008619392780035075,0.010675927452825128,0.016304897642258894,0.006048933012577384,0.006752502168795483,0.015398883405543589,0.007435699302597927,0.0020592838701602926,0.00900056677447762,0.009145953287345063,0.0036180430491003445,0.0063910354075241305,0.0034484434470824655,0.01124370518361076,0.010569518538279206,0.024735106682399522,0.0037805622039649585,0.00344153397713678,0.00970203254814363,0.00830374453025127,0.005877382507741477,0.006376837103529005,0.006338879769441919,0.004833636809004513,0.006055542188897483,0.010015395841520941,0.010015947675901887,0.010211028273489344,0.005358589052235203,0.010522617447638027,0.006377217214167565,0.006564313580534089,0.00448696107962692,0.014548878357366195,0.003958737894145132,0.012824545078714207,0.006559142188775579,0.012552905574152354,0.013163072152579915,0.005509543325368816,0.004827932619502603,0.006902517840307573,0.008490038744969088,0.009318351834983148,0.013528790284410086,0.0029190020204831924,0.0047567582607108165,0.0037866357000878245,0.01873036612481752,0.017875109142070914,0.012654719629180532,0.005667680943036409,0.005869267674403704,0.004309881385489801,0.0192487382443743,0.025945611480429974,0.004148519359573995,0.003428258867114271,0.006399018021252985,0.006731183540246223,0.0048348052603978744,0.01297601989397816,0.007620300314165223,0.01276289685657326,0.00743009572939816,0.005870828180992517,0.012816795217948103,0.0037840165034591644,0.007774793742915954,0.010546024922047745,0.012732904385276316,0.015602557842232054,0.008966923561347187,0.006230124807057905,0.012650599462113813,0.010902905678533633,0.00708456172132047,0.006906807221930247,0.006214994184775871,0.0067522210777040655,0.008138320338875732,0.007253172517251143,0.0064877833322014,0.004127969433766731,0.003441190778496945,0.01443760274094787,0.007405429389935283,0.008471606219978597,0.019361611507488018,0.014890331764721867,0.017578853419563552,0.003967630678961659,0.0037957570697144368,0.009616351255425804,0.01606030980102362,0.01872200549489498,0.006355705585633958,0.008200690667544554,0.008109111957232611,0.0051563806232713975,0.0041309922361432525,0.003109102214799927,0.005189603081090517,0.0034362215732483075,0.008806989946833745,0.008651429305104133,0.007419278771857064,0.0032793848339537717,0.007958575515482324,0.005882687881760529,0.016792184322089055,0.003766623279340652,0.015197930191907228,0.012527477727713524,0.0034576139843208627,0.014091979998913692,0.01645168090101069,0.0024035661674767107,0.005363823795766807,0.007247526889257323,0.011829681068932054,0.003981148910485886,0.011581760965906225,0.004461654897624339,0.0055107415552060925,0.004301659237773712,0.010735801929004852,0.018510210356275685,0.0041382922889518555,0.0037854730550762852,0.005869079983111309,0.006355083742877902,0.021086855185335537,0.007743908996629596,0.01057962943979075,0.018904955586213662,0.012630906496052238,0.011584308474456462,0.006406706292897621,0.004997806842643583,0.008295049950425652,0.003282234606151998,0.008656307370895244,0.0048240577720678616,0.01887306264467223,0.003441880058230448,0.008104426621843835,0.015187692566112418,0.012988140073182122,0.011772563981268355,0.00481586950873712,0.003769588391166356,0.004653730443773988,0.009875824668893148,0.020585191157579355,0.011010451378954908,0.004310248887984976,0.01456507938613711,0.005541176535009104,0.006918463823756958,0.005172271570515941,0.00520017276971431,0.005005790244381399,0.008463172533330487,0.004143687475255447,0.0193293276720275,0.022902155119444195,0.014152631825196911,0.007938797527401214,0.007952205209562966,0.008487926549158876,0.021534292044609125,0.012779252252152326,0.011024066577055562,0.03496417532199852,0.013861395781985884,0.01109780165391623,0.01003163230995805,0.006745862260125883,0.006742658982960088,0.021016523125175338,0.01589318686969775,0.04740276821024641,0.010090111512585952,0.010862143426547895,0.01715755794884348,0.026717766199367454,0.005951066882446591,0.009686948293967861,0.041917666558838114,0.008601993915431363,0.013162012322000781,0.0053537617093367225,0.012104519267765481,0.028026834515459675,0.008131612516275892,0.02303681716213813,0.013344188650730452,0.0036298808929882127,0.008317544765298343,0.012171492999628157,0.005511560939366562,0.010367815762223783,0.009187677428690843,0.016616984609963425,0.011066338094310532,0.005334660247686184,0.005862582081951412,0.01582935602862992,0.01179419476505178,0.007585508080283454,0.008051693787934364,0.006354842923191835,0.009186144606101076,0.007436615466701124,0.020354049756104494,0.010022780543081679,0.0210421857153364,0.007788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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - angle = observations[2] - cart_position = observations[0] - - if is_success: - return 100.0 - elif is_failure: - return -1.0 - else: - # Reward based on the angle to keep the pole upright - return np.exp(-angle**2) + (0.5 * (cart_position >= 0))";0.006791245015359342,0.006948361287382833,0.0043382070401347395,0.004494729867524855,0.0053996088347453616,0.013513287256950075,0.006014028669968026,0.004856930327986463,0.017161466560062805,0.004506012495166288,0.01007809294529957,0.007474555123594635,0.0058985260905789995,0.008552096257989751,0.00573698843271768,0.03503692425746776,0.004148667849396828,0.0038237238947784993,0.005898834271048031,0.008314688433953235,0.0031058866167622385,0.01658427171554674,0.00697503718090747,0.012221134010847162,0.013709523038555646,0.004683074091937806,0.0043209440291954206,0.008349955917252894,0.021131925369519595,0.006438090860091878,0.005383803722377302,0.006582322512853903,0.012532159002721381,0.004507011065838155,0.003125029516040075,0.0027678101585921363,0.008010366836876845,0.005318709708464833,0.013116652022409544,0.0038181098175494176,0.007986876309703584,0.00802003814358295,0.010572251290268618,0.0048639801597073215,0.011669977807095276,0.012889813602990746,0.013770625274294821,0.013784414920623779,0.004864060533741335,0.00414326085374798,0.008022551548781617,0.010608538778707276,0.016912349193073058,0.01314640105917964,0.008853833947793495,0.007300354224653823,0.006953939967478265,0.00399559002299406,0.008016543803666386,0.008870062427411608,0.01380054597464055,0.009067624161433405,0.007022546332938393,0.004172060220485444,0.021123307336249095,0.011080508505583226,0.007615180693366063,0.007964878472977641,0.01265782118559651,0.01744470521944078,0.008010956691706572,0.014819902653179395,0.005206957305115178,0.013415287767054836,0.01568718441714524,0.013796157230970779,0.004521568617083614,0.005162656707062687,0.0065552414724198245,0.006919582374177785,0.005535303558754198,0.009875000659596372,0.007127384920557755,0.0066120920448983185,0.005043615958759065,0.005196182613819051,0.010082639431620357,0.009737811617206006,0.005366335779565749,0.017937061712558763,0.005915383516318896,0.004490230361715769,0.006947489925160022,0.011330072928352919,0.005734927572359186,0.012862584587510923,0.004000077256683696,0.008269323939150856,0.004171185852845624,0.01221625588510792,0.004692833807994231,0.006251551075527973,0.007653664432025237,0.021957444797934787,0.011462454242723265,0.00447614163465261,0.011276965936693138,0.005374771818139654,0.006414993687740495,0.030524888030037438,0.011627321923500425,0.009356662482540705,0.010713639735057248,0.0038256467772769926,0.005907584626150692,0.005400109007096731,0.006782091099539549,0.008502083155301167,0.0162706581204358,0.00486647292353501,0.0034296036724998963,0.004695686950540513,0.008699787959695116,0.007494384698045951,0.007303219910641923,0.004341103719485735,0.007486373445946797,0.014605210480501856,0.006765650466700001,0.0048672221072080645,0.013977912433399818,0.027464807996571076,0.005043720083935822,0.00485839845332393,0.014111226387106419,0.009753265367106497,0.00975321746827293,0.004805589832786002,0.005550682402809953,0.004352641202315258,0.005541941126571171,0.01711530385572243,0.008899307347220511,0.004697548576796516,0.004843896669260731,0.021263339461387146,0.004687894864302479,0.014426229507551687,0.009587335772319835,0.006940760587769546,0.010093926713483962,0.006268255058032848,0.003991483412845919,0.004331718950072012,0.0036187523051234255,0.006437599557894873,0.007830431831865192,0.018498879987952778,0.0045205814836931394,0.004676250897878685,0.006268938873885979,0.008694880844204316,0.005032802364009445,0.0038167123195116393,0.004339165747289347,0.01765953697704158,0.016930731158332178,0.00503311421776593,0.010271162123539112,0.006269222823180383,0.0107713168180839,0.009225835407053485,0.003981110216042732,0.008891027339879606,0.004489716498020252,0.006920826888141474,0.00592374917804453,0.04280434910795209,0.010401564454293482,0.004882002455860195,0.004837168333675926,0.010100342631796594,0.006431664798496845,0.01885314673189985,0.006248287088302504,0.003829751947329484,0.008122926987789308,0.008677925992863076,0.01529813132794216,0.010280525980259487,0.011119439675699469,0.010904958717050548,0.005912067497811476,0.01289348451115653,0.016384403177874627,0.017466556166410456,0.030404178398714402,0.00959358745711555,0.025175606885831786,0.005714687591518664,0.030443965131743213,0.009757841469307384,0.018024627730343044,0.01529077314878466,0.01425819392940951,0.01362961346669596,0.0153159703562704,0.0121048586844809,0.005727465163670171,0.01686984723274511,0.008330092782311655,0.019669545721501366,0.01900181588287422,0.0404397797982908,0.009322224890883338,0.005900714827256994,0.013143546372182952,0.009420680202358128,0.016862742259970538,0.012861364200367105,0.00482944267967634,0.009571984853597114,0.014957492522192254,0.013776204371809282,0.010781598099216924,0.006257760830582234,0.013344056858579598,0.006559378178479949,0.006938551334946282,0.009953885092063004,0.005560509010265758,0.033419975616178904,0.01530507196284506,0.014301192326188518,0.014840001170711025,0.009049388125866788,0.0071374099898908454,0.0038016461431794753,0.014297732172890421,0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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - x, x_dot, theta, theta_dot = observations[:4] - - # Reward for successfully balancing the pole - if is_success: - return 10.0 - - # Penalty for falling the pole or reaching a failure condition - if is_failure: - return -50.0 - - # Small reward to encourage stability during training - small_reward = 0.1 * (np.cos(theta)**2 + np.sin(theta_dot)**2) - - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - x, x_dot, theta, theta_dot = observations[:4] - - # Reward for successfully balancing the pole - if is_success: - return 10.0 - - # Penalty for falling the pole or reaching a failure condition - if is_failure: - return -50.0 - - # Small reward to encourage stability during training - small_reward = 0.1 * (np.cos(theta)**2 + np.sin(theta_dot)**2) - - # Additional penalty for high cart velocity and angular velocity - penalty = -abs(x_dot) * 0.01 - abs(theta_dot) * 0.05 - - return small_reward + 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - x, x_dot, theta, theta_dot = observations[:4] - - # Reward for successfully balancing the pole - if is_success: - return 10.0 - - # Penalty for falling the pole or reaching a failure condition - if is_failure: - return -50.0 - - # Small reward to encourage stability during training - small_reward = 0.1 * (np.cos(theta)**2 + np.sin(theta_dot)**2) - - # Additional penalty for high cart velocity and angular velocity - penalty = -abs(x_dot) * 0.01 - abs(theta_dot) * 0.05 - - # Bonus reward for maintaining low cart position - bonus_reward = max(0, 1.0 - np.abs(x)) - - # Additional bonus for keeping the pole close to vertical - additional_bonus = max(0, 0.5 - np.abs(theta)) - - return small_reward + penalty + bonus_reward + 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - x, x_dot, theta, theta_dot = observations[:4] - - # Reward for successfully balancing the pole - if is_success: - return 10.0 - - # Penalty for falling the pole or reaching a failure condition - if is_failure: - return -50.0 - - # Small reward to encourage stability during training - small_reward = 0.1 * (np.cos(theta)**2 + np.sin(theta_dot)**2) - - # Additional penalty for high cart velocity and angular velocity - penalty = -abs(x_dot) * 0.01 - abs(theta_dot) * 0.05 - - # Bonus reward for maintaining low cart position - bonus_reward = max(0, 1.0 - np.abs(x)) - - return small_reward + penalty + 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - cart_position, cart_velocity, pole_angle, pole_angular_velocity = observations - - # Base reward for being alive and trying to balance the pole - reward = 1.0 - - # Penalize if the pole is too tilted - if abs(pole_angle) > np.pi / 15: - reward -= 0.5 - - # Additional penalty if cart velocity is too high - if abs(cart_velocity) > 2.0: - reward -= 0.1 - - # Penalize if the pole is moving too fast - if abs(pole_angular_velocity) > 3.0: - reward -= 0.15 - - # Success reward - if is_success: - reward += 10.0 - - # Failure penalty - if is_failure: - reward -= 5.0 - - return max(0, 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - cart_position, cart_velocity, pole_angle, pole_angular_velocity = observations - - # Base reward for being alive and trying to balance the pole - reward = 1.0 + 0.1 * np.clip(cart_position, -2.4, 2.4) # Encourage staying within the bounds - - # Penalize if the pole is too tilted - if abs(pole_angle) > np.pi / 15: - reward -= 0.5 - - # Additional penalty if cart velocity is too high - if abs(cart_velocity) > 2.0: - reward -= 0.2 - - # Penalize if the pole is moving too fast - if abs(pole_angular_velocity) > 3.0: - reward -= 0.25 - - # Success reward - if is_success: - reward += 15.0 - - # Failure penalty - if is_failure: - reward -= 7.5 - - return max(0, 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - x, x_dot, theta, theta_dot = observations - - if is_success: - return 100.0 - elif is_failure: - return -100.0 - else: - return 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - x, x_dot, theta, theta_dot = observations - - # Positional reward: closer to center of balance is better - position_reward = 1.0 - abs(x) / (2 * 2.4) - - # Angular reward: closer to vertical pole is better - angular_reward = np.cos(theta) - - # Stability penalty: penalize for high velocity and angular velocity - stability_penalty = -abs(x_dot) / 5 - abs(theta_dot) / 10 - - if is_success: - return 100.0 + position_reward + angular_reward - elif is_failure: - return -100.0 - else: - return position_reward + angular_reward + 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - x, x_dot, theta, theta_dot = observations - - # Positional reward: closer to center of balance is better - position_reward = 1.0 - (abs(x) / 2.4) ** 2 - - # Angular reward: closer to vertical pole is better - angular_reward = (np.cos(theta) + 1.0) / 2.0 - - # Stability penalty: penalize for high velocity and angular velocity - stability_penalty = -abs(x_dot) * 0.5 - abs(theta_dot) - - if is_success: - return 100.0 + position_reward + angular_reward - elif is_failure: - return -100.0 - else: - return position_reward + angular_reward + 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - x, x_dot, theta, theta_dot = observations - - # Positional reward: closer to center of balance is better - position_reward = 1.0 - (abs(x) / 2.4) ** 2 - - # Angular reward: closer to vertical pole is better - angular_reward = (np.cos(theta) + 1.0) / 2.0 - - # Stability penalty: penalize for high velocity and angular velocity - stability_penalty = -np.log(1 + abs(x_dot)) * 0.5 - np.log(1 + abs(theta_dot)) - - if is_success: - return 100.0 * (1 + position_reward + angular_reward) - elif is_failure: - return -100.0 - else: - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - x, dx, theta, dtheta = observations - - # Define constants - x_threshold = 2.4 - theta_threshold_radians = np.pi * 0.15 # Corresponds to ~20 degrees - - if is_success: - return 1000.0 # Large reward for successfully balancing the pole - elif is_failure: - return -100.0 # Penalty for failing to balance the pole - else: - # Reward based on proximity to goal - x_reward = 1.0 - np.abs(x) / x_threshold - theta_reward = 1.0 - np.abs(theta) / theta_threshold_radians - return x_reward + 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - if is_success: - return 1.0 - elif is_failure: - return -1.0 - else: - x, _, theta, _ = observations - return np.exp(-np.abs(x) - 2 * 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - cart_position = observations[0] - cart_velocity = observations[1] - pole_angle = observations[2] - pole_angular_velocity = observations[3] - - if is_success: - return 1.0 - elif is_failure: - return -1.0 - else: - # Reward based on stability of the cart and pole - reward = 0.5 * np.exp(-cart_position**2) - 0.1 * abs(cart_velocity) - reward -= 0.3 * abs(pole_angle) - reward -= 0.2 * abs(pole_angular_velocity) - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - if is_success: - return 10.0 - elif is_failure: - return -5.0 - else: - angle = observations[2] - cart_position = observations[0] - reward = np.exp(-np.abs(angle)) * (1.0 - np.abs(cart_position)) - return 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - if is_success: - return 10.0 # Strong positive reward for successful completion - elif is_failure: - return -5.0 # Strong negative reward for failure - - angle = observations[2] - cart_position = observations[0] - - # Exponential penalty for large angles - angle_penalty = np.exp(-0.1 * np.abs(angle)) - - # Linear reward for cart position centered around 0 - cart_position_reward = max(0, 1 - abs(cart_position) / 2.4) - - # Total reward - reward = angle_penalty + cart_position_reward - - return 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - if is_success: - return 10.0 # Strong positive reward for successful completion - elif is_failure: - return -5.0 # Strong negative reward for failure - - angle = observations[2] - cart_position = observations[0] - - # Exponential penalty for large angles - angle_penalty = np.exp(-angle * angle) # More aggressive penalty - - # Linear reward for cart position centered around 0 - cart_position_reward = max(0, (1 - abs(cart_position) / 2.4) ** 2) # Squared to emphasize closer proximity - - # Reward for time step completion - time_step_reward = 0.1 - - # Penalty for slowing down - velocity_penalty = -abs(observations[3]) * 0.05 # Penalize faster speed - - # Bonus for stability - stability_bonus = np.exp(-angle * angle) * 0.5 # Encourage long-term stability - - # Total reward - reward = angle_penalty + cart_position_reward + time_step_reward + velocity_penalty + stability_bonus - - return 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-CartPole-v1;qwen2.5-coder;"import numpy as np - -def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - if is_success: - return 10.0 # Strong positive reward for successful completion - elif is_failure: - return -5.0 # Strong negative reward for failure - - angle = observations[2] - cart_position = observations[0] - - # Exponential penalty for large angles - angle_penalty = np.exp(-angle * angle) # More aggressive penalty - - # Linear reward for cart position centered around 0 - cart_position_reward = max(0, (1 - abs(cart_position) / 2.4) ** 2) # Squared to emphasize closer proximity - - # Reward for time step completion - time_step_reward = 0.5 - - # Penalty for slowing down - velocity_penalty = -abs(observations[3]) * 0.05 # Penalize faster speed - - # Bonus for stability - stability_bonus = np.exp(-angle * angle) * 0.5 # Encourage long-term stability - - # Total reward - reward = (1 - angle_penalty) * cart_position_reward + time_step_reward + velocity_penalty + stability_bonus - - return 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-CartPole-v1;qwen2.5-coder;;;nan;0;0.0 -CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - cart_position = observations[0] - cart_velocity = observations[1] - pole_angle = observations[2] - pole_angular_velocity = observations[3] - - # Define the reward function to encourage balance - if is_success: - return 1.0 # Maximum reward for successful episode - elif is_failure: - return -1.0 # Minimum penalty for failed episode - - # Reward based on cart position and pole angle - cart_reward = np.exp(-np.abs(cart_position)) # Penalize moving the cart far from center - pole_reward = np.exp(-2 * np.abs(pole_angle)) # Encourage keeping the pole upright - - # Combine rewards - reward = cart_reward + pole_reward - - return reward";;nan;0;0.0 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - if is_success: - return 1.0 - elif is_failure: - return -1.0 - else: - pole_angle = observations[2] - cart_position = observations[0] - - # Penalize large angles and positions to encourage stability - angle_penalty = np.clip(pole_angle, -0.45, 0.45) - position_penalty = np.clip(cart_position, -1.5, 1.5) - - return -angle_penalty - 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - - x, x_dot, theta, theta_dot = observations - - if is_success: - return 10.0 - elif is_failure: - return -10.0 - else: - # Reward based on how close to vertical the pole is and how stable it is - proximity_to_vertical = np.cos(theta) - stability_factor = np.exp(-abs(theta_dot)) - - reward = proximity_to_vertical * stability_factor - - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - cart_position = observations[0] - cart_velocity = observations[1] - pole_angle = observations[2] - pole_angular_velocity = observations[3] - - if is_success: - return 1.0 - elif is_failure: - return -1.0 - else: - return 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - cart_pos, cart_vel, pole_angle, pole_vel = observations - - if is_success: - return 1.0 # High reward for successfully balancing the pole - elif is_failure: - return -1.0 # Low reward for failing to balance the pole - else: - return -0.05 * (np.abs(pole_angle) + cart_vel**2) # Penalize the agent for deviations from the 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-CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: - """"""Reward function for CartPole-v1 - - Args: - observations (np.ndarray): observation on the current state - is_success (bool): True if the goal is achieved, False otherwise - is_failure (bool): True if the episode ends unsuccessfully, False otherwise - - Returns: - float: The reward for the current step - """""" - angle = observations[2] - reward = -abs(angle) - if is_success: - reward += 100 - elif is_failure: - reward -= 50 - return 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diff --git a/src/log/CartPole_v1_log.csv b/src/log/CartPole_v1_log.csv new file mode 100644 index 0000000..bd9985d --- /dev/null +++ b/src/log/CartPole_v1_log.csv @@ -0,0 +1,1072 @@ +env;llm;reward_function;rewards;mean_reward;std_reward;SR +CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + terminated (bool): episode is terminated due a failure + truncated (bool): episode is truncated due a success + + Returns: + float: The reward for the current step + """""" + x, _, theta, _ = observations + if terminated or truncated: + return -1.0 + else: + return 1.0 - 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + terminated (bool): episode is terminated due a failure + truncated (bool): episode is truncated due a success + + Returns: + float: The reward for the current step + """""" + if terminated or truncated: + return -1.0 # Negative reward for failure or truncation + else: + return 1.0 # Positive reward for continuing the 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: + x, x_dot, theta, theta_dot = observations + reward = 1.0 - abs(theta) + if terminated or truncated: + reward -= 10.0 + return reward";0.0004890980954323817,0.006928776245389031,0.001481538048618689,0.005440801685071819,0.005672281447345117,0.0010390388510466285,0.003942045232937675,0.010673707224627118,0.004270238813483964,0.019693591365129912,0.003030756453363531,0.002050462453209492,0.0005796948190951127,0.01007636273329982,0.0017516960066329987,0.00319030278480954,0.021153630668219597,0.0021009595681324967,0.009997736533684907,0.009752428905163828,0.011761111540625107,0.0005869614187341358,0.001164994462080428,0.0010460291256893274,0.004538470600202309,0.005742882049159297,0.00032803679349991657,0.0059918783468383575,0.004386291347050374,0.012954138434513266,-0.0006469527753370165,0.0028544465329995404,0.0030505366278360995,0.017040254860049935,0.0027964311522734593,0.008144201462657664,0.012073963258269746,0.02885706931158607,0.014593642040832472,0.016991316085943207,0.0005498121749331379,0.007408199140179187,0.002858262913345251,0.00044021962938652396,0.007404464387872038,0.005200788401744633,0.003550635970875447,0.005021144043825879,0.009883172433666978,0.0009462702720383382,0.003751303809241422,0.004429860582175666,0.00048655903564236754,0.005172810121984639,0.024032145230128064,0.0027862653139064815,-0.0005646386021588254,0.0009922968345758762,0.005221572284595921,0.004761589905384174,0.005491476767899946,0.02175672529317085,0.011913476308884707,0.008198117982028576,0.0011802680871415767,0.0032532256209549244,0.018414170113426206,0.011349017935313793,0.014302583653074153,0.001002551361267665,0.003510914417857565,0.012849015050162711,0.00508935413009917,0.0011217558083384046,0.003923455394903017,0.007031328850831052,0.0011779396186483207,0.0017445002880943543,0.00988687282359573,0.007828948691212366,0.011871648286423479,0.00042149474164000626,0.012983128874127638,0.0009449108365277869,0.006793543766020482,0.00323174012650504,0.003484899071573663,0.004849787383792476,0.006412480605692879,-0.0006655315881956095,0.0020944600007740195,0.0022131925216175566,0.0021262882928836818,0.007276871346904072,0.006224902851051459,0.005434734534161334,0.020224510979213038,0.007957357316971244,0.0033788195575221694,0.004493871050200856,0.004746842602056112,4.759777185584498e-05,0.005979904344817018,-8.450188808301102e-05,0.004680366876839046,4.101761985086263e-05,-0.0004978757006680457,0.015089667598705746,0.0052958046907009984,0.009830643590951616,0.009749707286543643,0.001622411835819687,0.0064189544012675575,0.0016525805085241124,0.001988487573527269,0.0043177001792182755,0.0027375147329470285,-0.000744910003923865,0.007789611523828136,0.0009375601047092663,0.0004755812991364102,0.00940754797287916,0.0014411032675706462,0.0011537665201592968,0.027981463740413923,-0.0011065475691794699,0.002642492218418059,0.009646120888251938,0.010886626294283873,0.007845161473463664,0.0006193724105276769,0.001179115521496115,0.005093034869477522,0.003441860644767302,0.008146030285474747,0.0004101575913502835,0.004989537715986391,0.007359233342218191,0.0025920434125472746,0.0023209619482236635,0.009368697234871722,-0.00045397091065735003,0.010728046039543073,0.007355749560480391,0.004018433634829051,0.009170553083652793,0.010758391706320035,0.004943740977939188,0.003989749882919852,0.00426635951659724,-0.0006586840843659895,0.013075308180082092,0.005139351495006796,0.005106533475971973,0.005732823436716003,-0.00047924861353696704,0.013112280673268702,0.003508805061696196,0.04335824097486003,0.001463501347492973,-0.0005055082874607906,0.006829242416624917,0.00884160207003288,0.01629420241138356,0.0034056142133331497,0.0016891384272668967,0.0022320875518287713,0.019919231505520534,0.0004206686185893213,0.0016405949248509445,0.008066409594110948,0.013454766394748225,0.0030347176913064337,0.002676270332043635,0.004192209955821457,0.00047259723263425377,0.004466348350192511,0.000585695575491062,0.005885853158733277,0.0033367952730150936,0.005826966649979136,0.004671301817091787,0.009739560368352626,0.003703844112934401,-0.001051411533172797,0.009618519064345967,0.01542406198868497,0.021027237528116728,0.007095093217449824,0.013428832433044739,0.005269305975657007,0.0005610091628899233,0.004353803526821732,0.012651468832151689,0.003523185682275831,0.031969977412425105,0.016767580101276155,0.016551823283463316,0.008793432519330013,0.00890249394354008,0.004714080960972081,0.033654334321693936,0.031378667831193745,0.004589895638460989,0.0014156079478877565,-0.0007352611663209356,0.005639583348837333,0.018668459921585515,0.015164947396536978,0.009317126921659027,0.018355564172796088,0.0020376643453621102,0.006973206313740757,0.028568918092335333,0.01231924967115234,0.004682771165155081,0.02279251710813732,0.026080034017710998,0.003526637501436964,0.002895272377390399,0.013580719814658707,0.0033177534723212495,0.01167652159025252,0.02479015449962658,0.016569881861042482,0.014622985425199858,0.0012450020432188168,0.013205615118569025,0.004766892215137431,0.0003792393110126573,0.010201036571971816,0.0016398934175915535,0.019866109494420994,0.0038155227469995213,0.013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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: + x, x_dot, theta, theta_dot = observations + reward = 1.0 - abs(theta) + if terminated or truncated: + reward -= 10.0 + return reward";0.0004890980954323817,0.006928776245389031,0.001481538048618689,0.005440801685071819,0.005672281447345117,0.0010390388510466285,0.003942045232937675,0.010673707224627118,0.004270238813483964,0.019693591365129912,0.003030756453363531,0.002050462453209492,0.0005796948190951127,0.01007636273329982,0.0017516960066329987,0.00319030278480954,0.021153630668219597,0.0021009595681324967,0.009997736533684907,0.009752428905163828,0.011761111540625107,0.0005869614187341358,0.001164994462080428,0.0010460291256893274,0.004538470600202309,0.005742882049159297,0.00032803679349991657,0.0059918783468383575,0.004386291347050374,0.012954138434513266,-0.0006469527753370165,0.0028544465329995404,0.0030505366278360995,0.017040254860049935,0.0027964311522734593,0.008144201462657664,0.012073963258269746,0.02885706931158607,0.014593642040832472,0.016991316085943207,0.0005498121749331379,0.007408199140179187,0.002858262913345251,0.00044021962938652396,0.007404464387872038,0.005200788401744633,0.003550635970875447,0.005021144043825879,0.009883172433666978,0.0009462702720383382,0.003751303809241422,0.004429860582175666,0.00048655903564236754,0.005172810121984639,0.024032145230128064,0.0027862653139064815,-0.0005646386021588254,0.0009922968345758762,0.005221572284595921,0.004761589905384174,0.005491476767899946,0.02175672529317085,0.011913476308884707,0.008198117982028576,0.0011802680871415767,0.0032532256209549244,0.018414170113426206,0.011349017935313793,0.014302583653074153,0.001002551361267665,0.003510914417857565,0.012849015050162711,0.00508935413009917,0.0011217558083384046,0.003923455394903017,0.007031328850831052,0.0011779396186483207,0.0017445002880943543,0.00988687282359573,0.007828948691212366,0.011871648286423479,0.00042149474164000626,0.012983128874127638,0.0009449108365277869,0.006793543766020482,0.00323174012650504,0.003484899071573663,0.004849787383792476,0.006412480605692879,-0.0006655315881956095,0.0020944600007740195,0.0022131925216175566,0.0021262882928836818,0.007276871346904072,0.006224902851051459,0.005434734534161334,0.020224510979213038,0.007957357316971244,0.0033788195575221694,0.004493871050200856,0.004746842602056112,4.759777185584498e-05,0.005979904344817018,-8.450188808301102e-05,0.004680366876839046,4.101761985086263e-05,-0.0004978757006680457,0.015089667598705746,0.0052958046907009984,0.009830643590951616,0.009749707286543643,0.001622411835819687,0.0064189544012675575,0.0016525805085241124,0.001988487573527269,0.0043177001792182755,0.0027375147329470285,-0.000744910003923865,0.007789611523828136,0.0009375601047092663,0.0004755812991364102,0.00940754797287916,0.0014411032675706462,0.0011537665201592968,0.027981463740413923,-0.0011065475691794699,0.002642492218418059,0.009646120888251938,0.010886626294283873,0.007845161473463664,0.0006193724105276769,0.001179115521496115,0.005093034869477522,0.003441860644767302,0.008146030285474747,0.0004101575913502835,0.004989537715986391,0.007359233342218191,0.0025920434125472746,0.0023209619482236635,0.009368697234871722,-0.00045397091065735003,0.010728046039543073,0.007355749560480391,0.004018433634829051,0.009170553083652793,0.010758391706320035,0.004943740977939188,0.003989749882919852,0.00426635951659724,-0.0006586840843659895,0.013075308180082092,0.005139351495006796,0.005106533475971973,0.005732823436716003,-0.00047924861353696704,0.013112280673268702,0.003508805061696196,0.04335824097486003,0.001463501347492973,-0.0005055082874607906,0.006829242416624917,0.00884160207003288,0.01629420241138356,0.0034056142133331497,0.0016891384272668967,0.0022320875518287713,0.019919231505520534,0.0004206686185893213,0.0016405949248509445,0.008066409594110948,0.013454766394748225,0.0030347176913064337,0.002676270332043635,0.004192209955821457,0.00047259723263425377,0.004466348350192511,0.000585695575491062,0.005885853158733277,0.0033367952730150936,0.005826966649979136,0.004671301817091787,0.009739560368352626,0.003703844112934401,-0.001051411533172797,0.009618519064345967,0.01542406198868497,0.021027237528116728,0.007095093217449824,0.013428832433044739,0.005269305975657007,0.0005610091628899233,0.004353803526821732,0.012651468832151689,0.003523185682275831,0.031969977412425105,0.016767580101276155,0.016551823283463316,0.008793432519330013,0.00890249394354008,0.004714080960972081,0.033654334321693936,0.031378667831193745,0.004589895638460989,0.0014156079478877565,-0.0007352611663209356,0.005639583348837333,0.018668459921585515,0.015164947396536978,0.009317126921659027,0.018355564172796088,0.0020376643453621102,0.006973206313740757,0.028568918092335333,0.01231924967115234,0.004682771165155081,0.02279251710813732,0.026080034017710998,0.003526637501436964,0.002895272377390399,0.013580719814658707,0.0033177534723212495,0.01167652159025252,0.02479015449962658,0.016569881861042482,0.014622985425199858,0.0012450020432188168,0.013205615118569025,0.004766892215137431,0.0003792393110126573,0.010201036571971816,0.0016398934175915535,0.019866109494420994,0.0038155227469995213,0.013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+CartPole-v1;qwen2.5-coder;"import numpy as np + +def reward_func(observations:np.ndarray, terminated: bool, truncated: bool) -> float: + x, x_dot, theta, theta_dot = observations + if terminated or truncated: + return -1.0 + else: + return 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+CartPole-v1;qwen2.5-coder;"import numpy as np + +def reward_func(observations:np.ndarray, terminated: bool, truncated: bool) -> float: + x, x_dot, theta, theta_dot = observations + if terminated or truncated: + return -1.0 + else: + return 1.0 - 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: + x = observations[0] + theta = observations[2] + + if terminated or truncated: + return -1.0 + + # Positive reward based on cart position and pole angle + reward = 1.0 - abs(x) / 2.4 - abs(theta) / 0.2094 + + # Penalize for moving cart too far left or right + if abs(x) > 1.5: + reward -= 0.5 + + return 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: + x = observations[0] + theta = observations[2] + + if terminated or truncated: + return -1.0 + + # Positive reward based on cart position and pole angle + r_x = 1.0 - abs(x) / 2.4 + r_theta = 1.0 - abs(theta) / (np.pi / 18) + + # Combine rewards with different weights + reward = 0.5 * r_x + 0.5 * r_theta + + return 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + terminated (bool): episode is terminated due a failure + truncated (bool): episode is truncated due to a success + + Returns: + float: The reward for the current step + """""" + x, x_dot, theta, theta_dot = observations + return 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + terminated (bool): episode is terminated due a failure + truncated (bool): episode is truncated due to a success + + Returns: + float: The reward for the current step + """""" + x, x_dot, theta, theta_dot = observations + if terminated or truncated: + return -1.0 + else: + return 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + terminated (bool): episode is terminated due a failure + truncated (bool): episode is truncated due a success + + Returns: + float: The reward for the current step + """""" + x, _, theta, _ = observations + if terminated or truncated: + return -1.0 + else: + return 1.0 - 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, terminated: bool, truncated: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + terminated (bool): episode is terminated due a failure + truncated (bool): episode is truncated due to a success + + Returns: + float: The reward for the current step + """""" + x, _, theta, _ = observations + if terminated or truncated: + return -1.0 + else: + return 1.0 - 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+CartPole-v1;qwen2.5-coder;"import numpy as np + +def reward_func(observations:np.ndarray, is_success:bool) -> float: + x, x_dot, theta, theta_dot = observations + if is_success: + return 1.0 + else: + done = abs(x) > 2.4 or abs(theta) > 0.2095 # 2.4 is the horizontal limit and 0.2095 radians (12 degrees) is the angular limit + if done: + return -1.0 + else: + return 1.0 - (abs(x)**2 + 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+CartPole-v1;qwen2.5-coder;"import numpy as np + +def reward_func(observations:np.ndarray, is_success:bool) -> float: + x, x_dot, theta, theta_dot = observations + if is_success: + return 1.0 + else: + done = abs(x) > 2.4 or abs(theta) > 0.2095 # 2.4 is the horizontal limit and 0.2095 radians (12 degrees) is the angular limit + if done: + return -1.0 + else: + return 1.0 - (abs(x)**2 + 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise. + + Returns: + float: The reward for the current step + """""" + x, _, theta, _ = observations + r = np.cos(theta) * 0.5 + 0.5 + return r if not is_success else 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise. + + Returns: + float: The reward for the current step + """""" + x, _, theta, _ = observations + r = np.cos(theta) * 0.5 + 0.5 + return r if not is_success else 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: + x, _, theta, _ = observations + # Reward function logic + if is_success: + return 1.0 + elif abs(x) > 2 or abs(theta) >= np.pi / 6: + return -1.0 + else: + return 0.5 + +# 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: + x, _, theta, _ = observations + # Reward function logic + if is_success: + return 1.0 + elif abs(x) > 2 or abs(theta) >= np.pi / 6: + return -1.0 + else: + return 0.5 + +# 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise. + + Returns: + float: The reward for the current step + """""" + cart_position, cart_velocity, pole_angle, pole_angular_velocity = observations + + # Reward for maintaining balance + balance_reward = 1.0 - abs(pole_angle) / np.pi # Pole angle between -pi/2 and pi/2 + + # Penalty for cart movement + movement_penalty = abs(cart_position) * 0.1 + abs(cart_velocity) * 0.1 + + # Reward for success + if is_success: + success_reward = 100.0 + else: + success_reward = 0.0 + + return balance_reward - movement_penalty + 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise. + + Returns: + float: The reward for the current step + """""" + cart_position, cart_velocity, pole_angle, pole_angular_velocity = observations + + # Reward for maintaining balance + balance_reward = 1.0 - abs(pole_angle) / np.pi # Pole angle between -pi/2 and pi/2 + + # Penalty for cart movement + movement_penalty = abs(cart_position) * 0.1 + abs(cart_velocity) * 0.1 + + # Reward for success + if is_success: + success_reward = 100.0 + else: + success_reward = 0.0 + + return balance_reward - movement_penalty + 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+CartPole-v1;qwen2.5-coder;"import numpy as np + +def reward_func(observations: np.ndarray, is_success: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise. + + Returns: + float: The reward for the current step + """""" + theta = observations[2] # Angular position of the pole + angular_velocity = observations[3] # Angular velocity of the pole + + if is_success: + return 1.0 # Goal achieved + + # Reward based on how close to vertical and not swinging too fast + reward = np.cos(theta) - abs(angular_velocity) * 0.05 + return max(reward, -2.0) # Cap the minimum reward at 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+CartPole-v1;qwen2.5-coder;"import numpy as np + +def reward_func(observations: np.ndarray, is_success: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise. + + Returns: + float: The reward for the current step + """""" + theta = observations[2] # Angular position of the pole + angular_velocity = observations[3] # Angular velocity of the pole + + if is_success: + return 1.0 # Goal achieved + + # Reward based on how close to vertical and not swinging too fast + reward = np.cos(theta) - abs(angular_velocity) * 0.05 + return max(reward, -2.0) # Cap the minimum reward at 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise. + + Returns: + float: The reward for the current step + """""" + cart_position = observations[0] + cart_velocity = observations[1] + pole_angle = observations[2] + pole_angular_velocity = observations[3] + + # Define the threshold values for successful balance + position_threshold = 0.5 + angle_threshold = 0.2 + + if is_success: + return 1.0 # Maximum reward for successfully balancing the pole + + # Reward based on proximity to the target state + cart_position_reward = max(0, 1 - abs(cart_position) / position_threshold) + pole_angle_reward = max(0, 1 - abs(pole_angle) / angle_threshold) + + # Penalize for movement and rotation + velocity_penalty = abs(cart_velocity) * 0.01 + angular_velocity_penalty = abs(pole_angular_velocity) * 0.01 + + return cart_position_reward + pole_angle_reward - velocity_penalty - 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise. + + Returns: + float: The reward for the current step + """""" + cart_position = observations[0] + cart_velocity = observations[1] + pole_angle = observations[2] + pole_angular_velocity = observations[3] + + # Define the threshold values for successful balance + position_threshold = 0.5 + angle_threshold = 0.2 + + if is_success: + return 1.0 # Maximum reward for successfully balancing the pole + + # Reward based on proximity to the target state + cart_position_reward = max(0, 1 - abs(cart_position) / position_threshold) + pole_angle_reward = max(0, 1 - abs(pole_angle) / angle_threshold) + + # Penalize for movement and rotation + velocity_penalty = abs(cart_velocity) * 0.01 + angular_velocity_penalty = abs(pole_angular_velocity) * 0.01 + + return cart_position_reward + pole_angle_reward - velocity_penalty - 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+CartPole-v1;qwen2.5-coder;"import numpy as np + +def reward_func(observations:np.ndarray, is_success:bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise. + + Returns: + float: The reward for the current step + """""" + cart_position = observations[0] + cart_velocity = observations[1] + pole_angle = observations[2] + pole_angular_velocity = observations[3] + + # Reward for successfully balancing the pole + if is_success: + return 1.0 + + # Reward based on the angle of the pole (smaller angles are better) + angle_reward = np.cos(pole_angle) + + # Penalize large cart velocities and angular velocities to encourage stability + velocity_penalty = -abs(cart_velocity) * 0.01 + angular_penalty = -abs(pole_angular_velocity) * 0.01 + + return angle_reward + velocity_penalty + 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+CartPole-v1;qwen2.5-coder;"import numpy as np + +def reward_func(observations:np.ndarray, is_success:bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise. + + Returns: + float: The reward for the current step + """""" + cart_position = observations[0] + cart_velocity = observations[1] + pole_angle = observations[2] + pole_angular_velocity = observations[3] + + # Reward for successfully balancing the pole + if is_success: + return 1.0 + + # Reward based on the angle of the pole (smaller angles are better) + angle_reward = np.cos(pole_angle) + + # Penalize large cart velocities and angular velocities to encourage stability + velocity_penalty = -abs(cart_velocity) * 0.01 + angular_penalty = -abs(pole_angular_velocity) * 0.01 + + return angle_reward + velocity_penalty + 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode is finish without success, False otherwise + + Returns: + float: The reward for the current step + """""" + cart_position = observations[0] + cart_velocity = observations[1] + pole_angle = observations[2] + pole_angular_velocity = observations[3] + + if is_success: + return 1.0 + elif is_failure: + return -1.0 + else: + # Reward based on proximity to the goal and smoothness of control + distance_to_center = abs(cart_position) + angle_error = abs(pole_angle) + + cart_reward = np.exp(-distance_to_center ** 2 / (2 * 0.25 ** 2)) + pole_reward = np.exp(-angle_error ** 2 / (2 * np.pi / 8) ** 2) + + return cart_reward * 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode is finish without success, False otherwise + + Returns: + float: The reward for the current step + """""" + x, dx, theta, dtheta = observations + + # Reward for maintaining balance + balance_reward = np.exp(-0.1 * (x**2 + 0.5 * theta**2)) + + # Penalty for excessive movement or angular velocity + penalty = -np.abs(dx) / 2.4 - np.abs(dtheta) / (np.pi / 18) + + # Success reward + success_reward = 1 if is_success else 0 + + # Failure penalty + failure_penalty = -1 if is_failure else 0 + + return balance_reward + penalty + success_reward + 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode is finish without success, False otherwise + + Returns: + float: The reward for the current step + """""" + x, dx, theta, dtheta = observations + + # Reward for maintaining balance + balance_reward = np.exp(-0.1 * (x**2 + 0.5 * theta**2)) + + # Penalty for excessive movement or angular velocity + penalty = -np.abs(dx) / 2.4 - np.abs(dtheta) / (np.pi / 18) + + # Success reward + success_reward = 1 if is_success else 0 + + # Failure penalty + failure_penalty = -1 if is_failure else 0 + + return balance_reward + penalty + success_reward + 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode ends unsuccessfully, False otherwise + + Returns: + float: The reward for the current step + """""" + theta = observations[2] + reward = -np.abs(theta) # Penalize based on how close to vertical the pole is + if is_success: + reward += 100.0 # Large positive reward for success + elif is_failure: + reward -= 50.0 # Negative reward for failure + return 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode ends unsuccessfully, False otherwise + + Returns: + float: The reward for the current step + """""" + if is_success: + return 1.0 + elif is_failure: + return -1.0 + else: + x, dx, theta, dtheta = observations + reward = np.exp(-0.5 * (x**2 + dx**2 + theta**2 + dtheta**2)) + return 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode ends unsuccessfully, False otherwise + + Returns: + float: The reward for the current step + """""" + if is_success: + return 10.0 + elif is_failure: + return -10.0 + else: + cart_position, cart_velocity, pole_angle, pole_angular_velocity = observations + return np.cos(pole_angle) * (1 - abs(cart_position))";-0.00023294253991161537,0.0003392525675023479,0.011703996344616285,0.004736633321730253,0.006157517029779757,0.005210190380301307,0.020890831210730702,-0.00144320123880156,0.0083127296207583,0.008213162644292112,9.954277944783178e-05,0.01630298476016773,0.0003784674968947067,0.002616494253105286,0.004036468733100988,0.009385302527570527,-0.0008958387024577184,-0.0014287649110927593,0.014232603681842537,0.002198449875105853,0.002569994943286789,0.016247647219919448,0.0015387569740635005,0.018856135605158798,0.012600474056504996,0.0040329926511444986,0.002826032775254761,0.02083500151576652,-0.0008742609091239762,0.008498038267156614,0.003368058772565626,0.004131512391063557,-0.00027419258350681294,0.007786897635950458,0.0036790518244580544,-0.0014233801638680655,0.005716303988119921,0.008901443672064639,0.020631212331553034,0.00150984633192891,0.006560261458378373,0.011022920750315997,0.009090669410592705,0.004680954347057058,0.00982272894028714,0.014341073451289791,8.970552066263431e-05,0.0004353832328409875,0.004609481186803199,-0.0014375469419960398,0.001725565538247896,0.01041499222214002,0.0010685002995906125,-0.00027784313674465944,0.007235801547993979,0.01942412351640573,6.098562983096723e-05,0.0008965536481536913,0.009403704192769797,3.180250176462844e-05,0.0017929075129376995,0.0019581233046817516,0.0016636217287914292,-0.0008609695352607043,0.007253457714580089,0.016401673730539284,0.008433011809598186,0.006403413188860634,-0.001049321950988013,-0.0008321465747947802,0.0032767756880779757,0.003043707851470695,0.0025281060017675943,0.009801153562938331,0.004344267852908418,0.0003259993662010602,0.0018537150289254071,0.00374235062977346,-0.0014351239692885233,0.005762932200026987,0.007361744389538217,-0.0003370235426316698,0.014054519648119951,0.01628090046488447,0.005649892985348341,0.011549398657988649,0.006688173587828201,-0.00039311208250496503,0.00189425094454261,0.0008143059012975806,0.0033067436395584275,0.00020569331486380892,0.02700196478646188,0.0015606638245454854,0.00781115527694388,0.00850590854251416,0.0009115042461328056,-0.0009286143257854489,0.009827250140157813,0.00831008512081668,0.0005881289369239105,0.003516592931708632,0.01975453900571726,0.003991039295750018,0.01853847068077493,0.0021034802577893142,0.0015568933641168302,0.00687707458081191,0.005998958763293526,0.004519954374687808,-0.001116893481415707,0.029556436952198784,0.01174238288852769,0.001048448851201249,0.010956145454185112,0.00484688631107405,0.00223379688695415,0.02013552927408183,0.005406198457585872,0.0008814432234788306,0.00047041516308467,0.009284523655891592,0.0036193773682361924,0.001928139370531725,0.008726160099403524,0.0005544171380180949,0.006113242953594039,-0.001093623978260987,0.003988311202729622,0.000889801713638383,-0.00023411823251932623,0.003383316200631574,0.009470204995842987,0.00368584932816615,0.006924164208672697,0.0002960025715773758,0.010205053984575182,0.0039663682239805985,0.004613866310968496,0.005139540439037539,0.007071018998102397,0.02276773394313626,0.004081321419094307,-0.00021684486945402855,0.009461795732830786,0.0033495800560314065,0.0014939366622683198,0.0008193221922350802,0.0067358009395976776,0.009466520878999034,0.015585783760841862,0.0079312934359126,0.008747608024254724,0.010634634764980593,0.003343858855027667,0.0066797935768182215,0.002149828475626832,0.011038436651601067,0.002771073652585408,0.008165337479109901,0.003793277993951112,0.020999937908145007,-0.0011504450447667872,0.006821004216579132,0.00911429833796786,0.014945378262245207,0.004736334594485203,0.011406551764553284,0.00951194217633922,0.006175970471394649,0.004633563613135285,0.002284828733188021,0.0009892264815143555,0.022034642592737033,0.0014239368439662504,-0.000772673871899549,0.015638672049900326,0.0027843948731548856,0.0007732367592205686,0.01763632239205913,-0.0008341500210104618,0.00894885856583577,-0.0008345799919910188,0.002061320056915339,0.0022825493443236833,0.00581544519370244,0.00457819310398488,0.018311069815601832,0.02539300055391283,0.009552580678393692,0.001312313805318915,0.015794617856122083,0.0015450151555959492,0.005267348420952138,0.027269439630932476,0.03498674623323198,0.00734618102344141,0.010530468013379821,0.004783870807869105,0.015911464886073392,0.010703347532353915,0.002127642857652266,0.0025813722995459993,0.016460600123487056,0.0033149105978660773,0.008048620391979136,0.014039155582195841,0.01192305641207336,0.00726233705392278,0.02104111149510924,0.021682198338626762,0.014291620246781907,0.009010335049467673,0.0009962412380216691,0.013753312115766097,0.009537112539272351,0.002834456459838578,0.014757949087284328,0.0026286080015728982,0.012130616985940474,0.007627267638546749,0.00560065461765474,0.00481640122731036,0.0014867110088606186,0.01726929443515898,0.01461215420903042,0.02399870825396664,-0.0008312815778498007,0.021907036698670057,0.006322320877507772,0.0015861785582544526,0.007538135115172866,0.005089263866863568,0.019862653030674984,0.0067746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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode ends unsuccessfully, False otherwise + + Returns: + float: The reward for the current step + """""" + theta = observations[2] + x_dot = observations[3] + + if is_success: + return 1.0 + elif is_failure: + return -1.0 + else: + # Reward based on angle and velocity to encourage stability + return np.exp(-0.5 * (theta**2 + 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode ends unsuccessfully, False otherwise + + Returns: + float: The reward for the current step + """""" + cart_position, cart_velocity, pole_angle, pole_angular_velocity = observations + + # Base reward for being alive and trying to balance the pole + reward = 1.0 + + # Penalize if the pole is too tilted + if abs(pole_angle) > np.pi / 15: + reward -= 0.5 + + # Additional penalty if cart velocity is too high + if abs(cart_velocity) > 2.0: + reward -= 0.1 + + # Penalize if the pole is moving too fast + if abs(pole_angular_velocity) > 3.0: + reward -= 0.15 + + # Success reward + if is_success: + reward += 10.0 + + # Failure penalty + if is_failure: + reward -= 5.0 + + return max(0, 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+CartPole-v1;qwen2.5-coder;"import numpy as np + +def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode ends unsuccessfully, False otherwise + + Returns: + float: The reward for the current step + """""" + cart_position, cart_velocity, pole_angle, pole_angular_velocity = observations + + # Base reward for being alive and trying to balance the pole + reward = 1.0 + 0.1 * np.clip(cart_position, -2.4, 2.4) # Encourage staying within the bounds + + # Penalize if the pole is too tilted + if abs(pole_angle) > np.pi / 15: + reward -= 0.5 + + # Additional penalty if cart velocity is too high + if abs(cart_velocity) > 2.0: + reward -= 0.2 + + # Penalize if the pole is moving too fast + if abs(pole_angular_velocity) > 3.0: + reward -= 0.25 + + # Success reward + if is_success: + reward += 15.0 + + # Failure penalty + if is_failure: + reward -= 7.5 + + return max(0, 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode ends unsuccessfully, False otherwise + + Returns: + float: The reward for the current step + """""" + if is_success: + return 1.0 + elif is_failure: + return -1.0 + else: + x, _, theta, _ = observations + return np.exp(-np.abs(x) - 2 * 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode ends unsuccessfully, False otherwise + + Returns: + float: The reward for the current step + """""" + cart_position = observations[0] + cart_velocity = observations[1] + pole_angle = observations[2] + pole_angular_velocity = observations[3] + + if is_success: + return 1.0 + elif is_failure: + return -1.0 + else: + # Reward based on stability of the cart and pole + reward = 0.5 * np.exp(-cart_position**2) - 0.1 * abs(cart_velocity) + reward -= 0.3 * abs(pole_angle) + reward -= 0.2 * abs(pole_angular_velocity) + return 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode ends unsuccessfully, False otherwise + + Returns: + float: The reward for the current step + """""" + if is_success: + return 10.0 + elif is_failure: + return -5.0 + else: + angle = observations[2] + cart_position = observations[0] + reward = np.exp(-np.abs(angle)) * (1.0 - np.abs(cart_position)) + return 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+CartPole-v1;qwen2.5-coder;"import numpy as np + +def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode ends unsuccessfully, False otherwise + + Returns: + float: The reward for the current step + """""" + if is_success: + return 10.0 # Strong positive reward for successful completion + elif is_failure: + return -5.0 # Strong negative reward for failure + + angle = observations[2] + cart_position = observations[0] + + # Exponential penalty for large angles + angle_penalty = np.exp(-0.1 * np.abs(angle)) + + # Linear reward for cart position centered around 0 + cart_position_reward = max(0, 1 - abs(cart_position) / 2.4) + + # Total reward + reward = angle_penalty + cart_position_reward + + return 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+CartPole-v1;qwen2.5-coder;"import numpy as np + +def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode ends unsuccessfully, False otherwise + + Returns: + float: The reward for the current step + """""" + if is_success: + return 10.0 # Strong positive reward for successful completion + elif is_failure: + return -5.0 # Strong negative reward for failure + + angle = observations[2] + cart_position = observations[0] + + # Exponential penalty for large angles + angle_penalty = np.exp(-angle * angle) # More aggressive penalty + + # Linear reward for cart position centered around 0 + cart_position_reward = max(0, (1 - abs(cart_position) / 2.4) ** 2) # Squared to emphasize closer proximity + + # Reward for time step completion + time_step_reward = 0.5 + + # Penalty for slowing down + velocity_penalty = -abs(observations[3]) * 0.05 # Penalize faster speed + + # Bonus for stability + stability_bonus = np.exp(-angle * angle) * 0.5 # Encourage long-term stability + + # Total reward + reward = (1 - angle_penalty) * cart_position_reward + time_step_reward + velocity_penalty + stability_bonus + + return 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode ends unsuccessfully, False otherwise + + Returns: + float: The reward for the current step + """""" + + x, x_dot, theta, theta_dot = observations + + if is_success: + return 10.0 + elif is_failure: + return -10.0 + else: + # Reward based on how close to vertical the pole is and how stable it is + proximity_to_vertical = np.cos(theta) + stability_factor = np.exp(-abs(theta_dot)) + + reward = proximity_to_vertical * stability_factor + + return reward";0.0018976092968420438,0.0036188449141197386,0.014176615773515684,0.005637506026218698,0.01064402112973418,0.0019300029017399304,0.0057150901410796045,-0.0013305977969357253,-0.001806213873580475,0.00018493911421622156,-0.0012614211857301908,-0.002344192201262835,0.00766399495347233,0.014929310243559476,0.005037701419182238,0.0038340915957754654,-0.0052061032273504095,0.007629752305538132,-0.0014144070793597654,-0.0016740329585961224,0.011110560106635138,-0.0031851531208457345,-0.0026278447648846624,0.008401125350096727,0.005787916431337359,-0.0028572615657948925,-0.0031574387719160977,-0.0008585121960246434,-0.0036952265124178363,-0.002702871286163832,-5.9687855933691674e-05,0.003816850429385201,-0.002602325719686034,0.004271009527289456,-0.0034274101364667993,0.011469786893881744,-0.0043067217602038525,0.011738360856195083,0.004035745965442034,0.027941684728614264,-0.0005625816905919634,-0.001242819489315506,-0.00031369755699251395,0.004117077794910425,-0.001007621466615381,0.004449066525940587,0.0008164926860664901,-0.00398408396462808,-0.0028052836256768953,0.0032197431633699107,0.00016890050562132168,0.0053024711628245624,0.0030588999733083323,-0.002678900523432041,-8.34836173909192e-05,0.0019828312787816146,0.0027645891238964205,-0.0049462723965418365,-0.0014988114403359232,0.004851350780578323,0.008079247496293001,-0.002007737257725698,0.006312116843835111,0.0018485390095496752,0.011503926213031337,0.0008502147729848732,0.014004053873642253,0.012857682779979295,0.00147072661689325,0.0012638841505965774,-0.00408733589174861,0.005892690489176268,-0.0016763157578103427,-0.004163911375831785,-0.0012448038369833726,-0.0016386190689926213,0.0016854635043842717,-0.003657433483733703,-0.0005405304167179099,-0.0031908931049258516,0.0025956561682537564,0.002375691750347165,0.004082163886123371,-0.0020512109883829796,-0.004817366279367275,0.005502509515243404,-0.0029318520888759357,0.00258330121433698,0.015719618504367736,0.014438139546058727,6.76349104854022e-05,0.020067526902674788,0.0020305465199794716,0.0023407910514401456,0.008332104257783184,0.039327903540214725,0.006131172241688565,0.00016245008460635457,0.002675822616229824,-0.002783544729644886,0.00019859161542857455,0.01523552080854873,-0.004914056004014313,0.005227122790523675,0.007724239088123218,-0.001455344863527057,0.011528698794349566,0.008079444288973255,0.01648956118126068,0.016631682956182076,0.003902641791686544,0.029584152196961342,-0.0047983127411449335,-0.002468317986012897,-0.004695888985368008,0.004700695103602034,0.01407491666444312,0.016154542161476076,0.032894866026420194,0.02370290034030821,-0.0032141127754228655,0.007935014954687937,0.007584701058532017,0.00721864642807177,0.002165494881637837,0.005018664791461806,0.009781041448147404,0.019550323453871983,0.003768835077030623,0.016372701912884438,-0.003593743791797628,0.004276031363946807,0.0045716256900446605,0.007786522275735258,0.004084370054632807,0.02439419221612408,0.0026804929453497633,0.0241518235015096,0.005168862323203485,0.02478680339565782,0.009915635158285986,0.006089333283937788,-0.0032646100080787614,-0.003203915830390459,0.0036719394988492127,0.025941461248590827,-0.000701747338664426,0.020344079075889743,0.0029680794658791397,0.0030046618242625736,0.015137327415060131,0.020616802847539764,0.0047727839186765325,0.0036480545513739885,0.005275693566019918,0.0074884389567202265,0.03253781561716153,0.03089580390256083,0.004294067535243678,0.004903668770737314,0.010577124524237218,0.014572986949804441,0.029774336648903128,0.027426081102972934,0.01670496586606988,0.03201070839710554,0.021669051072000248,0.004893487236372604,0.03340271811713492,0.007205164495634243,0.018605221653883768,0.044261323609982486,0.038535688366478214,0.0018132246862600703,0.01695558634897682,-0.0032785429834431795,0.022352570487367482,0.020149081207551572,0.002377532598413029,0.027802790555688583,0.016572489437358767,0.02053759863340812,0.0138809908013208,0.005282265231364945,0.011044967645642664,0.02197619112187655,0.003106077614136729,0.007281041366217918,0.011834716329129273,0.0027441326634283203,0.0058652247725575505,0.007537092877665056,0.07218753071953854,0.021133851055770328,0.1175697048163023,0.04528067247101676,0.034303783527010655,0.018345121682721878,0.03257896741890922,0.01141982629263025,0.001706215689895979,0.005777093400870943,0.020790330171626615,0.06340566601166148,0.031644387342486584,0.11778051353490578,0.049423402796370856,0.05756243954976017,0.022045709540660766,0.05115419873832896,0.018402483940048526,0.05299604900269168,0.05324707418313289,0.05480488947253587,0.06946837596524773,0.023455401223717202,0.044008946403731465,0.042471744093578054,0.04547073127705317,0.08978059863459163,0.13476659985273517,0.0594854741392773,0.03534916925229443,0.08515238486968629,0.06553671714388279,0.006624525636066414,0.10820741125657177,0.2211981496375472,0.14083703004573828,0.07140352422607377,0.18173306837385206,0.08150210971840838,0.17233497712158136,0.10745251265903005,0.20002380299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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode ends unsuccessfully, False otherwise + + Returns: + float: The reward for the current step + """""" + cart_position = observations[0] + cart_velocity = observations[1] + pole_angle = observations[2] + pole_angular_velocity = observations[3] + + if is_success: + return 1.0 + elif is_failure: + return -1.0 + else: + return 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + x, x_dot, theta, theta_dot = observations + + if is_success: + return 10.0 + elif is_failure: + return -10.0 + else: + # Reward based on how close to vertical the pole is and how stable it is + proximity_to_vertical = np.cos(theta) + stability_factor = np.exp(-abs(theta_dot)) + + reward = proximity_to_vertical * stability_factor + + return reward";0.25001582844859394,0.09082634030358688,0.001885421748948312,0.03411720960366207,-0.013914501808352166,-0.039916701104991764,-0.053740416380008565,-0.021582664537603533,-0.04372212304572779,0.03043269919485856,0.03926547348336027,0.09829803312339738,0.034526905502986635,0.029043466672184885,0.20396321065587397,0.08950049554076274,0.05377458708541134,-0.04808006608821043,-0.06243242744652155,-0.012347669723775068,0.09066704163610781,0.05133539449202793,0.22892369147002498,0.035282834384108924,0.03448219667786342,-0.05264145913348296,-0.00022996982553642772,-0.014459770194238613,-0.050676144562439795,0.02039551369760967,-0.008256382320271862,-0.046548095343347994,-0.02379235654832719,0.11453165159456781,0.0222537178818915,0.08585117806622021,-0.040425787684256254,0.002042315300381183,0.05137580652177565,0.08484265718018139,0.12938140357603278,-0.020654326196653458,0.09379852595113067,0.31098751766860244,0.30931279012324897,0.10956022669233827,-0.05631484099849055,-0.00832071729204701,0.01739411620317354,0.005893876595514117,0.4525414096821838,0.10921226128385518,-0.00919996092109437,0.019759315922503087,0.07359728795286968,0.1022360282808387,0.28642130277002903,0.02762781833710836,0.30338833702009504,0.09407173494548611,-0.03652651773116465,-0.05872612152241596,0.03037810178687029,0.08987003725465574,0.019746027932559366,0.06245449009529091,-0.042872842418111706,-0.05863596834029386,-0.06014029428991029,0.012202182335086596,-0.021373396289079163,0.02853138349721337,-0.00417770296899876,0.02291880913934722,0.17971969974152546,-0.028286932307302715,-0.024183794058131043,0.1666313770049132;0.049654997192026595;0.10175899996743606;0.0 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + x, x_dot, theta, theta_dot = observations + + if is_success: + return 10.0 + elif is_failure: + return -10.0 + else: + # Reward based on how close to vertical the pole is and how stable it is + proximity_to_vertical = np.cos(theta) + stability_factor = np.exp(-abs(theta_dot)) + + reward = proximity_to_vertical * stability_factor + + return 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + x, x_dot, theta, theta_dot = observations + + if is_success: + return 10.0 + elif is_failure: + return -10.0 + else: + # Reward based on how close to vertical the pole is and how stable it is + proximity_to_vertical = np.cos(theta) + stability_factor = np.exp(-abs(theta_dot)) + + reward = proximity_to_vertical * stability_factor + + return 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + x, x_dot, theta, theta_dot = observations + + if is_success: + return 10.0 + elif is_failure: + return -10.0 + else: + # Reward based on how close to vertical the pole is and how stable it is + proximity_to_vertical = np.cos(theta) + stability_factor = np.exp(-abs(theta_dot)) + + reward = proximity_to_vertical * stability_factor + + return 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + x, x_dot, theta, theta_dot = observations + + if is_success: + return 10.0 + elif is_failure: + return -10.0 + else: + # Reward based on how close to vertical the pole is and how stable it is + proximity_to_vertical = np.cos(theta) + stability_factor = np.exp(-abs(theta_dot)) + + reward = proximity_to_vertical * stability_factor + + return reward";0.00011712262759067155,-0.0057773862246767,-0.0006489268674540296,0.030326659389370618,-0.002959440260630591,0.004223561836472067,-0.005580263943318049,0.007050510315325471,0.0005593868690421424,0.00549263776218323,0.0013265552066645226,0.02789765412922396,0.01324867847755096,0.017597044695299473,0.013090039622856622,0.010492261576118773,0.03469671799128299,0.004321123334698112,0.030866605461659626,0.0035170470630266656,-0.005617388783791883,0.007798462588495823,0.010678205288243484,-0.0052346708231183325,-0.007855053156059383,-0.0036642213837802915,-0.0031465666407435814,-0.0057563592977398865,-0.0035427887446152274,-0.002956958575273008,0.007084913055146922,-0.0039604960031329305,-0.0007455137004249229,0.0030246380034721037,0.0010331013080645676,-0.006229508860009779,-0.004238024118423989,-0.004879049459403586,0.0032184396608437443,0.0046485335545170805,-0.0064093331585931505,-0.005663506321581442,-0.005089911074193987,-0.0007556876755033901,0.017268815218695756,-0.002372929950780375,0.006848069952673112,0.004215404633462951,0.004345134763452257,0.014686759279415406,0.03281236987163634,-0.0034508497245934076,0.0008251865808994458,0.007396858070253241,0.011072377028135476,0.004398187894327108,0.045146628080299164,0.009173677235379898,0.006066926341600769,-0.003980258871405513,0.05384364841744641,-0.005948502400425582,0.020219647804192022,-0.006312839962779341,0.004661056549947689,-0.0022151315108538162,-0.0025865461803181587,0.01315890596001291,0.00022758698084962396,0.017426560830051917,0.002902230988031353,0.010134259583946563,0.004588609148013655,-7.710228316462992e-05,-0.0014524867406308477,-0.0022136311919508833,0.0016483433856535913,-0.0067315729421945246,0.017431341362678224,0.011644397915394778,0.0393623689867017,0.005218075888331385,0.03058194355242121,0.020153260895229383,0.02979840784743585,0.06378294533387245,0.005727524020680631,0.0637306674802318,0.004448711064653122,0.03958279989875435,0.03864481426044274,0.01175397061860774,0.004270697441108396,-0.005040503427086926,0.0005955138556112805,0.027494265568915092,0.012293000547888493,0.0005633861495885428,0.02454883174465521,0.08281990737364231,0.009430751946439874,0.06435563899656851,-0.0014230446223145125,0.12240348198729245,0.03384869926777691,0.05262123241274056,0.03364966698835629,0.08562545729796625,0.08642462913511298,0.016302388814967116,0.014971777367560462,0.026579024988857673,0.05930163023131435,0.18105103965782562,0.1212142415125308,0.028112743982210267,0.07037783987694718,0.06451809922199181,0.049918335773584115,0.061351036673604954,0.03680613070717858,0.06693103436530193,0.04442931837738594,0.2237872027288039,0.18072349102465293,0.2471653959349237,0.19986515726028783,0.07541559568727593,0.18206701598028144,0.08808593200015422,0.08052597709833563,0.19785207803609495,0.22234129531643887,0.19843194057640753,0.10730114463444078,0.2192556283800846,0.3224273606722992,0.24256213281003916,0.330158829135683,0.18581860963377966,0.2982185553817276,0.16566512088214685;0.042487325240132226;0.07236767728113142;0.06 +CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + x, x_dot, theta, theta_dot = observations + + if is_success: + return 10.0 + elif is_failure: + return -10.0 + else: + # Reward based on how close to vertical the pole is and how stable it is + proximity_to_vertical = np.cos(theta) + stability_factor = np.exp(-abs(theta_dot)) + + reward = proximity_to_vertical * stability_factor + + return 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + x, x_dot, theta, theta_dot = observations + + if is_success: + return 10.0 + elif is_failure: + return -10.0 + else: + # Reward based on how close to vertical the pole is and how stable it is + proximity_to_vertical = np.cos(theta) + stability_factor = np.exp(-abs(theta_dot)) + + reward = proximity_to_vertical * stability_factor + + return 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode ends unsuccessfully, False otherwise + + Returns: + float: The reward for the current step + """""" + cart_position = observations[0] + cart_velocity = observations[1] + pole_angle = observations[2] + pole_angular_velocity = observations[3] + + if is_success: + return 1.0 + elif is_failure: + return -1.0 + else: + # Reward based on proximity to center, stability of the pole + reward = 1.0 - np.abs(cart_position) - np.abs(pole_angle) + if cart_velocity != 0: + reward -= 0.1 * abs(1 / cart_velocity) + if pole_angular_velocity != 0: + reward -= 0.1 * abs(1 / pole_angular_velocity) + return max(reward, 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode ends unsuccessfully, False otherwise + + Returns: + float: The reward for the current step + """""" + if is_success: + return 1.0 + elif is_failure: + return -1.0 + else: + x, x_dot, theta, theta_dot = observations + return -abs(theta) / 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+CartPole-v1;qwen2.5-coder:14b;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + pole_angle = observations[2] + cart_position = observations[0] + + if is_success: + return 1.0 + + if is_failure: + return -1.0 + + angle_penalty = np.abs(pole_angle) / (np.pi/2) + position_penalty = np.abs(cart_position) / 4.8 + velocity_penalty = np.abs(observations[3]) / 5.0 + + reward = 1.0 - (angle_penalty + position_penalty + velocity_penalty) + + return max(reward, 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode ends unsuccessfully, False otherwise + + Returns: + float: The reward for the current step + """""" + cart_pos = observations[0] + cart_vel = observations[1] + pole_angle = observations[2] + pole_ang_vel = observations[3] + + if is_success: + return 1.0 + elif is_failure: + return -1.0 + + # Reward based on pole angle and angular velocity + angle_reward = np.clip(1 - abs(pole_angle) / (np.pi / 2), 0, 1) + ang_vel_penalty = -abs(pole_ang_vel) + + # Penalize large cart positions and velocities + pos_vel_penalty = -abs(cart_pos * cart_vel) + + return angle_reward + ang_vel_penalty - 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+CartPole-v1;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for CartPole-v1 + + Args: + observations (np.ndarray): observation on the current state + is_success (bool): True if the goal is achieved, False otherwise + is_failure (bool): True if the episode ends unsuccessfully, False otherwise + + Returns: + float: The reward for the current step + """""" + return 1.0 if is_success else -1.0 if is_failure else observations[2]**2 + 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diff --git a/src/main.py b/src/main.py index b8caf6c..aae1554 100644 --- a/src/main.py +++ b/src/main.py @@ -40,14 +40,14 @@ def main(): memory. """ parse_logger() - env_type = LunarLander(Algo.PPO) + env_type = CartPole(Algo.PPO) model = 'qwen2.5-coder' human_feedback = False LoggerCSV(env_type, model) viral = VIRAL( - env_type=env_type, model=model, hf=human_feedback, training_time=500_000, numenvs=3, options=additional_options) - viral.generate_context(Prompt.LUNAR_LANDER) - viral.generate_reward_function(n_init=1, n_refine=3) + env_type=env_type, model=model, hf=human_feedback, training_time=30_000, numenvs=3, options=additional_options) + viral.generate_context(Prompt.CARTPOLE) + viral.generate_reward_function(n_init=1, n_refine=0) for state in viral.memory: viral.logger.info(state) diff --git a/src/main3.py b/src/main3.py index ad33e73..fd7558c 100644 --- a/src/main3.py +++ b/src/main3.py @@ -37,15 +37,27 @@ def main(): memory. """ parse_logger() - env_type = LunarLander(Algo.PPO) + env_type = CartPole(Algo.PPO) model = 'qwen2.5-coder' human_feedback = True LoggerCSV(env_type, model) viral = VIRAL( - env_type=env_type, model=model, hf=human_feedback, training_time=2_000, numenvs=1, options=additional_options) - viral.test_reward_func("""def reward_function(observations, is_success, is_failure): - # Your reward calculation logic here - return 0.0271 # Placeholder return value""") + env_type=env_type, model=model, hf=human_feedback, training_time=50_000, numenvs=2, options=additional_options) + viral.test_reward_func("""def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + x, x_dot, theta, theta_dot = observations + + if is_success: + return 10.0 + elif is_failure: + return -10.0 + else: + # Reward based on how close to vertical the pole is and how stable it is + proximity_to_vertical = np.cos(theta) + stability_factor = np.exp(-abs(theta_dot)) + + reward = proximity_to_vertical * stability_factor + + return reward""") for state in viral.memory: viral.logger.info(state)