feat: add actor path to parser

src/reinforcement_learning/config/test.yaml

@@ -1,12 +1,12 @@
 test:
   ros__parameters:
-    environment: 'CarBeat'
+    environment: 'CarTrack'
     track: 'multi_track' # track_1, track_2, track_3, multi_track, multi_track_testing -> only applies for CarTrack
-    max_steps_evaluation: 1000000
+    number_eval_episodes: 100
     max_steps: 3000
-    actor_path: results/23_09_08_06:28:51/models/actor_checkpoint.pht
-    critic_path: results/23_09_08_06:28:51/models/critic_checkpoint.pht
-    algorithm: 'TD3'
+    actor_path: training_logs/SAC-CarTrack-23_10_25_10:41:10/models/SAC-checkpoint-15000_actor.pht
+    critic_path: training_logs/SAC-CarTrack-23_10_25_10:41:10/models/SAC-checkpoint-15000_critic.pht
+    algorithm: 'SAC'
     step_length: 0.1
     reward_range: 3.0
     collision_range: 0.2

src/reinforcement_learning/launch/test.launch.py

@@ -32,6 +32,9 @@ def generate_launch_description():
             os.path.join(pkg_environments, f'{env_launch[env]}.launch.py')),
         launch_arguments={
             'track': TextSubstitution(text=str(config['test']['ros__parameters']['track'])),
+            'car_name': TextSubstitution(text=str(config['test']['ros__parameters']['car_name']) if 'car_name' in config['test']['ros__parameters'] else 'f1tenth'),
+            'car_one': TextSubstitution(text=str(config['test']['ros__parameters']['car_name']) if 'car_name' in config['test']['ros__parameters'] else 'f1tenth'),
+            'car_two': TextSubstitution(text=str(config['test']['ros__parameters']['ftg_car_name']) if 'ftg_car_name' in config['test']['ros__parameters'] else 'ftg_car'),
         }.items() #TODO: this doesn't do anything
     )
 

src/reinforcement_learning/reinforcement_learning/parse_args.py

@@ -7,11 +7,13 @@
 def parse_args():
     param_node = __declare_params()
 
-    env_params = __get_env_params(param_node)
-    algorithm_params = __get_algorithm_params(param_node)
-    network_params = __get_network_params(param_node)
+    env_params, rest_env = __get_env_params(param_node)
+    algorithm_params, rest_alg = __get_algorithm_params(param_node)
+    network_params, rest_params = __get_network_params(param_node)
 
-    return env_params, algorithm_params, network_params
+    rest = {**rest_env, **rest_alg, **rest_params}
+
+    return env_params, algorithm_params, network_params, rest
 
 
 def __declare_params():
@@ -92,7 +94,12 @@ def __get_env_params(param_node: Node):
         case _:
             raise Exception(f'Environment {params_dict["environment"]} not implemented')
 
-    return config
+    # Collect all the parameters that were not used into a python dictionary
+    rest = set(params_dict.keys()).difference(set(config.dict().keys()))
+    rest = {key: params_dict[key] for key in rest}
+
+    param_node.get_logger().info(f'Rest: {rest}')
+    return config, rest  
 
 def __get_algorithm_params(param_node: Node):
     params = param_node.get_parameters([
@@ -114,7 +121,11 @@ def __get_algorithm_params(param_node: Node):
 
     config = cfg.TrainingConfig(**params_dict)
 
-    return config
+    rest = set(params_dict.keys()).difference(set(config.dict().keys()))
+    rest = {key: params_dict[key] for key in rest}
+
+    param_node.get_logger().info(f'Rest: {rest}')
+    return config, rest
 
 def __get_network_params(param_node: Node):
     params = param_node.get_parameters([
@@ -144,5 +155,9 @@ def __get_network_params(param_node: Node):
         case _:
             raise Exception(f'Algorithm {params_dict["algorithm"]} not implemented')
 
-    return config
+    rest = set(params_dict.keys()).difference(set(config.dict().keys()))
+    param_node.get_logger().info(f'Rest: {rest}')
+    rest = {key: params_dict[key] for key in rest}
+
+    return config, rest
 

src/reinforcement_learning/reinforcement_learning/test.py

@@ -16,7 +16,7 @@
 def main():
     rclpy.init()
 
-    env_config, algorithm_config, network_config = parse_args()
+    env_config, algorithm_config, network_config, rest = parse_args()
 
     print(
         f'Environment Config: ------------------------------------- \n'
@@ -30,26 +30,14 @@ def main():
     env_factory = EnvironmentFactory()
     network_factory = NetworkFactory()
 
-    match network_config['algorithm']:
-        case 'PPO':
-            config = cfg.PPOConfig(**network_config)
-        case 'DDPG':
-            config = cfg.DDPGConfig(**network_config)
-        case 'SAC':
-            config = cfg.SACConfig(**network_config)
-        case 'TD3':
-            config = cfg.TD3Config(**network_config)
-        case _:
-            raise Exception(f'Algorithm {network_config["algorithm"]} not implemented')
-
     env = env_factory.create(env_config['environment'], env_config)
-    agent = network_factory.create_network(env.OBSERVATION_SIZE, env.ACTION_NUM, config=config)
+    agent = network_factory.create_network(env.OBSERVATION_SIZE, env.ACTION_NUM, config=network_config)
 
     # Load models if both paths are provided
-    if network_config['actor_path'] and network_config['critic_path']:
+    if rest['actor_path'] and rest['critic_path']:
         print('Reading saved models into actor and critic')
-        agent.actor_net.load_state_dict(torch.load(network_config['actor_path']))
-        agent.critic_net.load_state_dict(torch.load(network_config['critic_path']))
+        agent.actor_net.load_state_dict(torch.load(rest['actor_path']))
+        agent.critic_net.load_state_dict(torch.load(rest['critic_path']))
         print('Successfully Loaded models')
     else:
         raise Exception('Both actor and critic paths must be provided')
@@ -58,7 +46,7 @@ def main():
         case 'PPO':
             ppo_evaluate(env, agent, algorithm_config)
         case _:
-            off_policy_evaluate(env, agent, algorithm_config)
+            off_policy_evaluate(env, agent, algorithm_config['number_eval_episodes'])
 
 if __name__ == '__main__':
     main()

src/reinforcement_learning/reinforcement_learning/train.py

@@ -19,7 +19,7 @@
 def main():
     rclpy.init()
 
-    env_config, algorithm_config, network_config = parse_args()
+    env_config, algorithm_config, network_config, _ = parse_args()
 
     # Set Seeds
     torch.manual_seed(algorithm_config['seed'])