feat: add more params to config

src/environments/environments/CarGoalEnvironment.py

@@ -37,14 +37,13 @@ class CarGoalEnvironment(Node):
             When the number of steps surpasses MAX_STEPS
     """
 
-    def __init__(self, car_name, reward_range=1, max_steps=50, collision_range=0.5, step_length=0.5):
+    def __init__(self, car_name, reward_range=1, max_steps=50, step_length=0.5):
         super().__init__('car_goal_environment')
 
         # Environment Details ----------------------------------------
         self.NAME = car_name
         self.REWARD_RANGE = reward_range
         self.MAX_STEPS = max_steps
-        self.COLLISION_RANGE = collision_range
         self.STEP_LENGTH = step_length
 
         self.step_counter = 0

src/reinforcement_learning/config/car_goal.yaml

@@ -1,11 +1,12 @@
 meta:
   ros__parameters:
-    mode: evaluation # training
+    mode: training # training
 
 car_goal_training:
   ros__parameters:
     max_steps_exploration: 5000
     max_steps_training: 1000000
+    reward_range: 2.0
     # gamma: 0.95
     # tau: 0.005
     # g: 5

src/reinforcement_learning/config/car_wall.yaml

@@ -1,11 +1,13 @@
 meta:
   ros__parameters:
-    mode: evaluation # training
+    mode: training # training
 
 car_wall_training:
   ros__parameters:
     max_steps_exploration: 5000
     max_steps_training: 1000000
+    reward_range: 1.0
+    collision_range: 2.0
     # gamma: 0.95
     # tau: 0.005
     # g: 5

src/reinforcement_learning/launch/car_goal.launch.py

@@ -33,7 +33,7 @@ def generate_launch_description(): #TODO: include the train launch file here
         'car_goal.yaml'
     )
 
-    config = yaml.load(open(config_path))
+    config = yaml.load(open(config_path), Loader=yaml.Loader)
     mode = config['meta']['ros__parameters']['mode']
 
     # Launch the Environment

src/reinforcement_learning/launch/car_wall.launch.py

@@ -35,7 +35,7 @@ def generate_launch_description():
 
 
 
-    config = yaml.load(open(config_path))
+    config = yaml.load(open(config_path), Loader=yaml.Loader)
     mode = config['meta']['ros__parameters']['mode']
 
     # Launch the Environment

src/reinforcement_learning/reinforcement_learning/car_goal_training.py

@@ -30,7 +30,8 @@
         ('max_steps_training', 1_000_000),
         ('max_steps_exploration', 1_000),
         ('max_steps', 100),
-        ('step_length', 0.25)
+        ('step_length', 0.25),
+        ('reward_range', 0.2)
     ]
 )
 
@@ -47,6 +48,7 @@
     'critic_lr',
     'max_steps',
     'step_length',
+    'reward_range'
     ])
 
 MAX_STEPS_TRAINING,\
@@ -60,21 +62,25 @@
 ACTOR_LR,\
 CRITIC_LR,\
 MAX_STEPS,\
-STEP_LENGTH = [param.value for param in params]
+STEP_LENGTH,\
+REWARD_RANGE = [param.value for param in params]
 
 print(
-    f'Exploration Steps: {MAX_STEPS_EXPLORATION}\n',
-    f'Training Steps: {MAX_STEPS_TRAINING}\n',
-    f'Gamma: {GAMMA}\n',
-    f'Tau: {TAU}\n',
-    f'G: {G}\n',
-    f'Batch Size: {BATCH_SIZE}\n',
-    f'Buffer Size: {BUFFER_SIZE}\n',
-    f'Seed: {SEED}\n',
-    f'Actor LR: {ACTOR_LR}\n',
-    f'Critic LR: {CRITIC_LR}\n',
-    f'Steps per Episode: {MAX_STEPS}\n',
+    f'---------------------------------------------\n'
+    f'Exploration Steps: {MAX_STEPS_EXPLORATION}\n'
+    f'Training Steps: {MAX_STEPS_TRAINING}\n'
+    f'Gamma: {GAMMA}\n'
+    f'Tau: {TAU}\n'
+    f'G: {G}\n'
+    f'Batch Size: {BATCH_SIZE}\n'
+    f'Buffer Size: {BUFFER_SIZE}\n'
+    f'Seed: {SEED}\n'
+    f'Actor LR: {ACTOR_LR}\n'
+    f'Critic LR: {CRITIC_LR}\n'
+    f'Steps per Episode: {MAX_STEPS}\n'
     f'Step Length: {STEP_LENGTH}\n'
+    f'Reward Range: {REWARD_RANGE}\n'
+    f'---------------------------------------------\n'
 )
 MAX_ACTIONS = np.asarray([3, 1])
 MIN_ACTIONS = np.asarray([0, -1])

src/reinforcement_learning/reinforcement_learning/car_wall_training.py

@@ -29,7 +29,9 @@
         ('max_steps_training', 1_000_000),
         ('max_steps_exploration', 1_000),
         ('max_steps', 100),
-        ('step_length', 0.25)
+        ('step_length', 0.25),
+        ('reward_range', 0.2),
+        ('collision_range', 0.2)
     ]
 )
 
@@ -46,6 +48,8 @@
     'critic_lr',
     'max_steps',
     'step_length',
+    'reward_range',
+    'collision_range'
     ])
 
 MAX_STEPS_TRAINING,\
@@ -59,21 +63,27 @@
 ACTOR_LR,\
 CRITIC_LR,\
 MAX_STEPS,\
-STEP_LENGTH = [param.value for param in params]
+STEP_LENGTH,\
+REWARD_RANGE,\
+COLLISION_RANGE = [param.value for param in params]
 
 print(
-    f'Exploration Steps: {MAX_STEPS_EXPLORATION}\n',
-    f'Training Steps: {MAX_STEPS_TRAINING}\n',
-    f'Gamma: {GAMMA}\n',
-    f'Tau: {TAU}\n',
-    f'G: {G}\n',
-    f'Batch Size: {BATCH_SIZE}\n',
-    f'Buffer Size: {BUFFER_SIZE}\n',
-    f'Seed: {SEED}\n',
-    f'Actor LR: {ACTOR_LR}\n',
-    f'Critic LR: {CRITIC_LR}\n',
-    f'Steps per Episode: {MAX_STEPS}\n',
+    f'---------------------------------------------\n'
+    f'Exploration Steps: {MAX_STEPS_EXPLORATION}\n'
+    f'Training Steps: {MAX_STEPS_TRAINING}\n'
+    f'Gamma: {GAMMA}\n'
+    f'Tau: {TAU}\n'
+    f'G: {G}\n'
+    f'Batch Size: {BATCH_SIZE}\n'
+    f'Buffer Size: {BUFFER_SIZE}\n'
+    f'Seed: {SEED}\n'
+    f'Actor LR: {ACTOR_LR}\n'
+    f'Critic LR: {CRITIC_LR}\n'
+    f'Steps per Episode: {MAX_STEPS}\n'
     f'Step Length: {STEP_LENGTH}\n'
+    f'Reward Range: {REWARD_RANGE}\n'
+    f'Collision Range: {COLLISION_RANGE}\n'
+    f'---------------------------------------------\n'
 )
 MAX_ACTIONS = np.asarray([3, 3.14])
 MIN_ACTIONS = np.asarray([-0.5, -3.14])
@@ -86,7 +96,7 @@
 def main():
     time.sleep(3)
 
-    env = CarWallEnvironment('f1tenth', step_length=STEP_LENGTH, max_steps=MAX_STEPS)
+    env = CarWallEnvironment('f1tenth', step_length=STEP_LENGTH, max_steps=MAX_STEPS, reward_range=REWARD_RANGE, collision_range=COLLISION_RANGE)
 
     actor = Actor(observation_size=OBSERVATION_SIZE, num_actions=ACTION_NUM, learning_rate=ACTOR_LR)
     critic = Critic(observation_size=OBSERVATION_SIZE, num_actions=ACTION_NUM, learning_rate=CRITIC_LR)