Adapted docstring

cooperative_cuisine/reinforcement_learning/gym_env.py

@@ -36,7 +36,6 @@ class SimpleActionSpace(Enum):
 
 
 def get_env_action(player_id, simple_action, duration):
-
     """
 
     Args:
@@ -103,7 +102,6 @@ visualizer.set_grid_size(40)
 
 
 def shuffle_counters(env):
-
     """
     Shuffles the counters of an environment
     Args:
@@ -160,7 +158,8 @@ class EnvGymWrapper(Env):
         config_env = OmegaConf.to_container(config.environment, resolve=True)
         config_item_info = OmegaConf.to_container(config.item_info, resolve=True)
         for val in config_env['hook_callbacks']:
-            config_env['hook_callbacks'][val]["callback_class"] = instantiate(config_env['hook_callbacks'][val]["callback_class"])
+            config_env['hook_callbacks'][val]["callback_class"] = instantiate(
+                config_env['hook_callbacks'][val]["callback_class"])
         config_env["orders"]["order_gen_class"] = instantiate(config_env["orders"]["order_generator"])
         self.config_env = config_env
         self.config_item_info = config_item_info
@@ -223,6 +222,7 @@ class EnvGymWrapper(Env):
         and additional information
         """
         # this is simply a work-around to enable no action which is necessary for the play_gym.py
+        # but not for the rl agent
         if action == 8:
             observation = self.get_observation()
             reward = self.env.score - self.prev_score
@@ -240,19 +240,14 @@ class EnvGymWrapper(Env):
             self.env.step(
                 timedelta(seconds=self.global_step_time / self.in_between_steps)
             )
-
         observation = self.get_observation()
-
         reward = self.env.score - self.prev_score
         self.prev_score = self.env.score
-
         if reward > 0:
             print("- - - - - - - - - - - - - - - - SCORED", reward)
-
         terminated = self.env.game_ended
         truncated = self.env.game_ended
         info = {}
-
         return observation, reward, terminated, truncated, info
 
     def reset(self, seed=None, options=None):
@@ -272,16 +267,12 @@ class EnvGymWrapper(Env):
 
         if self.randomize_counter_placement:
             shuffle_counters(self.env)
-
         self.player_name = str(0)
         self.env.add_player(self.player_name)
         self.player_id = list(self.env.players.keys())[0]
-
         info = {}
         obs = self.get_observation()
-
         self.prev_score = 0
-
         return obs, info
 
     def get_observation(self):

cooperative_cuisine/reinforcement_learning/obs_converter/advanced_converter_array.py

@@ -52,7 +52,7 @@ class AdvancedStateConverterArray(StateToObservationConverter):
             setup is chosen.
 
 
-        Returns: An encoding for the environment state that is not onehot
+        Returns: An encoding for the environment state
 
         """
         if player is not None:

cooperative_cuisine/reinforcement_learning/train_single_agent.py

@@ -27,7 +27,6 @@ def main(cfg: DictConfig):
     config: dict[str, Any] = OmegaConf.to_container(cfg.model, resolve=True)
     env_info: dict[str, Any] = OmegaConf.to_container(cfg.environment, resolve=True)
     debug: bool = additional_configs["debug_mode"]
-
     vec_env = additional_configs["vec_env"]
     number_envs_parallel = config["number_envs_parallel"]
     model_class = instantiate(cfg.model.model_type)