From 7e2e51760753411b79f79a1ad8fb78d36a10c2ba Mon Sep 17 00:00:00 2001 From: valentincuzin Date: Mon, 6 Jan 2025 21:16:21 +0100 Subject: [PATCH] some test with the hopper --- src/Environments/Hopper.py | 2 + src/Environments/Prompt.py | 2 +- src/PolicyTrainer/PolicyTrainer.py | 3 +- src/log/Hopper_v5_log.csv | 245 +++++++++++++++++++++++++++++ src/log/LunarLander_v3_log.csv | 54 +++++++ src/main.py | 6 +- src/test_function.py | 39 ++--- src/visualise.py | 4 +- 8 files changed, 323 insertions(+), 32 deletions(-) diff --git a/src/Environments/Hopper.py b/src/Environments/Hopper.py index cd666e8..bc7a176 100644 --- a/src/Environments/Hopper.py +++ b/src/Environments/Hopper.py @@ -28,6 +28,8 @@ def success_func(self, env: gym.Env, info: dict) -> tuple[bool|bool]: if info["terminated"]: return False, True + elif info["x_position"] > 5.0: + return True, False else: return False, False diff --git a/src/Environments/Prompt.py b/src/Environments/Prompt.py index f1d169a..d720d3b 100644 --- a/src/Environments/Prompt.py +++ b/src/Environments/Prompt.py @@ -27,7 +27,7 @@ class Prompt(Enum): """, } HOPPER = { - "Goal": "move forward as quickly as possible", + "Goal": "Control the Hopper to move in the forward direction", "Observation Space": """Box(-inf, inf, (11,), float64) The observation space consists of the following parts (in order): diff --git a/src/PolicyTrainer/PolicyTrainer.py b/src/PolicyTrainer/PolicyTrainer.py index baa28e4..a95b782 100644 --- a/src/PolicyTrainer/PolicyTrainer.py +++ b/src/PolicyTrainer/PolicyTrainer.py @@ -66,7 +66,7 @@ def _learning(self, state: State, queue: Queue = None) -> None: ) vec_env, model, numvenv = self._generate_env_model(state.reward_func, self.numenvs) training_callback = TrainingInfoCallback() - policy = model.learn(total_timesteps=self.timeout, callback=training_callback, progress_bar=True) # , progress_bar=True + policy = model.learn(total_timesteps=self.timeout, callback=training_callback) # , progress_bar=True policy.save(f"model/{self.env_name}_{state.idx}.pth") metrics = training_callback.get_metrics() #self.logger.debug(f"{state.idx} TRAINING METRICS: {metrics}") @@ -212,6 +212,7 @@ def test_policy_hf(self, policy_path: str, nb_episodes: int = 5): actions, _ = policy.predict(obs) obs, _, term, trunc, info = env.step(actions) if term or trunc: + self.logger.info(info) info["TimeLimit.truncated"] = trunc info["terminated"] = term is_success, _ = self.success_func(env, info) diff --git a/src/log/Hopper_v5_log.csv b/src/log/Hopper_v5_log.csv index 7540cb3..7a299ef 100644 --- a/src/log/Hopper_v5_log.csv +++ b/src/log/Hopper_v5_log.csv @@ -81,3 +81,248 @@ Hopper-v5;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:boo # Reward based on height and velocity return 0.5 * z_coord + 0.1 * 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+Hopper-v5;llama3.2-vision;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for Hopper-v5 + + 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 0.1 * 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+Hopper-v5;llama3.2-vision;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for Hopper-v5 + + 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 0.1 * observations[5] + 0.05 * np.sum(observations[8:11]) - 0.01 * 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+Hopper-v5;llama3.2-vision;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for Hopper-v5 + + 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 + """""" + vx = observations[5] # velocity of the x-coordinate of the torso + vz = observations[6] # velocity of the z-coordinate (height) of torso + + reward = vz # Reward forward velocity as quickly as possible + + if is_success: + reward += 10.0 # Bonus for achieving goal + + if is_failure: + reward -= 1.0 # Penalty for failure + + return 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+Hopper-v5;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> float: + """"""Reward function for Hopper-v5 + + 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 # High reward for successfully achieving the goal + elif is_failure: + return -5.0 # Negative reward for failing to achieve the goal or ending unsuccessfully + + x_velocity = observations[5] + z_position = observations[0] + + # Penalize high vertical position to encourage movement forward + vertical_penalty = -np.abs(z_position) + + # Reward based on horizontal velocity + return max(0.1 * x_velocity, 0) + 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+Hopper-v5;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for Hopper-v5 + + 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_position = observations[0] + x_velocity = observations[5] + + if is_success: + return 100.0 + elif is_failure: + return -100.0 + else: + return 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+Hopper-v5;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for Hopper-v5 + + 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 + """""" + z_coordinate = observations[0] # torso height + torso_angle = observations[1] # angle of the torso + torso_velocity = observations[5] # velocity of the x-coordinate of the torso + + if is_success: + return 10.0 # Large positive reward for achieving the goal + elif is_failure: + return -10.0 # Large negative reward for failure + + if z_coordinate > 1.2 or z_coordinate < 0.5: # Penalize if the robot falls too high or too low + return -1.0 + + if abs(torso_angle) > np.pi / 4: # Penalize if the torso is tilted too far forward or backward + return -1.0 + + if abs(torso_velocity) < 0.1: # Penalize if the torso is not moving enough forward + return -0.5 + + return 0.1 # Small positive reward for maintaining stability and 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+Hopper-v5;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for Hopper-v5 + + 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 + """""" + torso_height = observations[0] + torso_x_velocity = observations[5] + + if is_success: + return 1.0 + elif is_failure: + return -1.0 + else: + # Penalize low height and negative x-velocity + height_reward = max(0, 1 - abs(torso_height)) + velocity_reward = max(0, torso_x_velocity) + + return height_reward + 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+Hopper-v5;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> float: + """"""Reward function for Hopper-v5 + + 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 + """""" + torso_height = observations[0] + torso_angle = observations[1] + thigh_hinge_angle = observations[2] + leg_hinge_angle = observations[3] + foot_hinge_angle = observations[4] + torso_velocity = observations[5] + torso_height_velocity = observations[6] + + # Base reward + reward = 0.0 + + # Positive rewards for successful actions + if is_success: + reward += 10.0 + + # Penalize falling or unsuccessful episodes + if is_failure: + reward -= 10.0 + + # Reward for maintaining torso height above a certain threshold + if torso_height > -0.2: + reward += 0.5 * (torso_height + 0.2) + + # Reward for maintaining the torso at an optimal angle + if -np.pi / 4 < torso_angle < np.pi / 4: + reward += 0.1 + + # Reward for moving forward in small steps + if abs(torso_velocity) > 0.5: + reward += 0.2 * abs(torso_velocity) + + # Penalty for excessive vertical movement + if abs(torso_height_velocity) > 5.0: + reward -= 0.1 + + return 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+Hopper-v5;qwen2.5-coder;"import numpy as np + +def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> float: + """"""Reward function for Hopper-v5 + + 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 + """""" + torso_height = observations[0] + torso_angle = observations[1] + thigh_hinge_angle = observations[2] + leg_hinge_angle = observations[3] + foot_hinge_angle = observations[4] + torso_velocity = observations[5] + torso_height_velocity = observations[6] + + # Base reward + reward = 0.0 + + # Positive rewards for successful actions + if is_success: + reward += 10.0 + + # Penalize falling or unsuccessful episodes + if is_failure: + reward -= 20.0 + + # Reward for maintaining torso height above a certain threshold + if torso_height > -0.2: + reward += 0.5 * (torso_height + 0.2) + + # Reward for maintaining the torso at an optimal angle + if -np.pi / 4 < torso_angle < np.pi / 4: + reward += 0.1 + + # Reward for moving forward in small steps + if abs(torso_velocity) > 0.5: + reward += 0.2 * abs(torso_velocity) + + # Penalty for excessive vertical movement + if abs(torso_height_velocity) > 5.0: + reward -= 0.1 + + # Penalize lateral movements to encourage forward motion + if abs(thigh_hinge_angle) > np.pi / 8 or abs(leg_hinge_angle) > np.pi / 8 or abs(foot_hinge_angle) > np.pi / 8: + reward -= 0.1 + + return 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diff --git a/src/log/LunarLander_v3_log.csv b/src/log/LunarLander_v3_log.csv index f11003e..f7e31c8 100644 --- a/src/log/LunarLander_v3_log.csv +++ b/src/log/LunarLander_v3_log.csv @@ -1241,3 +1241,57 @@ def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> total_reward = landing_reward + altitude_penalty + distance_penalty - angular_penalty - angular_velocity_penalty + leg_contact_reward return max(total_reward, 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+LunarLander-v3;qwen2.5-coder;"def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> float: + """"""Reward function for LunarLander-v3 + + 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, y = observations[0], observations[1] + vx, vy = observations[2], observations[3] + angle = observations[4] + + # Reward for proximity to landing area and minimal velocity + position_reward = -abs(x) - abs(y) - abs(vx) - abs(vy) + angle_reward = np.cos(angle) + + # Penalize if not in contact with both legs + leg_contact_penalty = 0.5 * (observations[6] + observations[7]) + + # Final reward calculation + if is_success: + return 1000.0 + elif is_failure: + return -1000.0 + else: + return position_reward + angle_reward - 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+LunarLander-v3;qwen2.5-coder;"def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + """"""Reward function for LunarLander-v3 + + 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, y, vx, vy, angle, w, _, _ = observations + + if is_success: + return 100.0 + elif is_failure: + return -100.0 + else: + # Penalize large velocities and angles to encourage stability + velocity_penalty = abs(vx) + abs(vy) + abs(angle) + + # Reward proximity to the target (center of the landing pad) + proximity_reward = 10 * (1 - np.sqrt(x**2 + y**2)) + + return proximity_reward - 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diff --git a/src/main.py b/src/main.py index ace69ea..99f99d9 100644 --- a/src/main.py +++ b/src/main.py @@ -38,18 +38,18 @@ def main(): memory. """ parse_logger() - env_type = LunarLander(Algo.PPO) + env_type = Hopper(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=300_000, numenvs=2, options=additional_options) - viral.generate_context(Prompt.LUNAR_LANDER.value) + viral.generate_context(Prompt.HOPPER.value) viral.generate_reward_function(n_init=1, n_refine=2) for state in viral.memory: viral.logger.info(state) if __name__ == "__main__": - for _ in range(10): + for _ in range(3): main() diff --git a/src/test_function.py b/src/test_function.py index ce9b794..67c5fac 100644 --- a/src/test_function.py +++ b/src/test_function.py @@ -5,7 +5,7 @@ from log.LoggerCSV import LoggerCSV from RLAlgo.DirectSearch import DirectSearch from RLAlgo.Reinforce import Reinforce -from Environments import Prompt, Algo, CartPole, LunarLander +from Environments import Prompt, Algo, CartPole, LunarLander, Hopper from VIRAL import VIRAL from LLM.LLMOptions import additional_options @@ -37,36 +37,25 @@ def main(): memory. """ parse_logger() - env_type = LunarLander(Algo.PPO) + env_type = Hopper(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=400_000, numenvs=2, options=additional_options) viral.test_reward_func(""" -def reward_func(observations: np.ndarray, is_success: bool, is_failure: bool) -> float: - x, y, v_x, v_y, theta, omega, leg_1, leg_2 = observations - - # Penalty for altitude and horizontal distance - altitude_penalty = -abs(y) * 0.5 # Scaled down to emphasize landing stability over altitude - distance_penalty = abs(x) - - # Reward for landing safely - landing_reward = 100 if is_success else -50 if is_failure else 0 - - # Penalty for angular deviation from vertical - angular_penalty = abs(theta) * 1.0 # Scaled down to make it less significant - - # Penalize large angular velocity - angular_velocity_penalty = abs(omega) - - # Reward for maintaining leg contact with the ground - leg_contact_reward = 5 if leg_1 == 1 and leg_2 == 1 else -2 # Adjusted weights to be more punitive - - # Final reward calculation - total_reward = landing_reward + altitude_penalty + distance_penalty - angular_penalty - angular_velocity_penalty + leg_contact_reward - - return max(total_reward, 0)""") +def reward_func(observations:np.ndarray, is_success:bool, is_failure:bool) -> float: + if is_success: + return 10.0 + elif is_failure: + return -5.0 + else: + # Reward based on joint angles and torques to promote efficient movement + joint_angles = observations[:9] + torques = observations[9:18] + angle_reward = np.sum(np.exp(-np.abs(joint_angles))) + torque_reward = np.sum(np.exp(-np.abs(torques))) + return 0.5 * (angle_reward + torque_reward)""") viral.policy_trainer.test_policy_hf("model/LunarLander-v3_1.pth", 5) for state in viral.memory: viral.logger.info(state) diff --git a/src/visualise.py b/src/visualise.py index 7d46af8..411ddfb 100644 --- a/src/visualise.py +++ b/src/visualise.py @@ -43,9 +43,9 @@ def main(): memory. """ parse_logger() - env_type = LunarLander(Algo.PPO) + env_type = Hopper(Algo.PPO) p = PolicyTrainer([], env_type, 1, 2) - p.test_policy_hf("model/LunarLander-v3_1.pth", 5) + p.test_policy_hf("model/Hopper-v5_1.pth", 5) if __name__ == "__main__": main()