alles was ich hab

environments/factory/base_factory.py

@@ -113,15 +113,19 @@ class BaseFactory(gym.Env):
     def movement_actions(self):
         return self._actions.movement_actions
 
-    def __init__(self, level='simple', n_agents=1, max_steps=int(5e2), pomdp_radius: Union[None, int] = None, **kwargs):
+    def __init__(self, level='simple', n_agents=1, max_steps=int(5e2), pomdp_radius: Union[None, int] = None,
+                 allow_square_movement=True, allow_diagonal_movement=True, allow_no_op=True, **kwargs):
+        self.allow_no_op = allow_no_op
+        self.allow_diagonal_movement = allow_diagonal_movement
+        self.allow_square_movement = allow_square_movement
         self.n_agents = n_agents
         self.max_steps = max_steps
         self.pomdp_radius = pomdp_radius
 
         self.done_at_collision = False
-        _actions = Actions(allow_square_movement=kwargs.get('allow_square_movement', True),
+        _actions = Actions(allow_square_movement=self.allow_square_movement,
-                           allow_diagonal_movement=kwargs.get('allow_diagonal_movement', True),
+                           allow_diagonal_movement=self.allow_diagonal_movement,
-                           allow_no_op=kwargs.get('allow_no_op', True))
+                           allow_no_op=allow_no_op)
         self._actions = _actions + self.additional_actions
 
         self._level = h.one_hot_level(
@@ -165,12 +169,16 @@ class BaseFactory(gym.Env):
             pos = self._agent_states[0].pos
             # pos = [agent_state.pos for agent_state in self.agent_states]
             # obs = [] ... list comprehension... pos per agent
-            npad = [(0, 0)] + [(self.pomdp_radius, self.pomdp_radius)] * (self._state.ndim - 1)
+            x0, x1 = max(0, pos[0] - self.pomdp_radius), pos[0] + self.pomdp_radius + 1
-            x_roll = self.pomdp_radius-pos[0]
+            y0, y1 = max(0, pos[1] - self.pomdp_radius), pos[1] + self.pomdp_radius + 1
-            y_roll = self.pomdp_radius-pos[1]
+            obs = self._state[:, x0:x1, y0:y1]
-            padded_state = np.pad(self._state, pad_width=npad, mode='constant', constant_values=0)
+            if obs.shape[1] != self.pomdp_radius ** 2 + 1 or obs.shape[2] != self.pomdp_radius ** 2 + 1:
-            padded_state = np.roll(np.roll(padded_state, x_roll, axis=1), y_roll, axis=2)
+                obs_padded = np.full((obs.shape[0], self.pomdp_radius ** 2 + 1, self.pomdp_radius ** 2 + 1), 1)
-            obs = padded_state[:, :self.pomdp_radius * 2 + 1, :self.pomdp_radius * 2 + 1]
+                a_pos = np.argwhere(obs[h.AGENT_START_IDX] == h.IS_OCCUPIED_CELL)[0]
+                obs_padded[:,
+                    abs(a_pos[0]-self.pomdp_radius):abs(a_pos[0]-self.pomdp_radius)+obs.shape[1],
+                    abs(a_pos[1]-self.pomdp_radius):abs(a_pos[1]-self.pomdp_radius)+obs.shape[2]] = obs
+                obs = obs_padded
         else:
             obs = self._state
         return obs
@@ -211,8 +219,7 @@ class BaseFactory(gym.Env):
 
         info.update(step_reward=reward, step=self.steps)
 
-        obs = self._return_state()
+        return None, reward, done, info
-        return obs, reward, done, info
 
     def _is_moving_action(self, action):
         return action in self._actions.movement_actions

environments/factory/simple_factory.py

@@ -150,7 +150,7 @@ class SimpleFactory(BaseFactory):
                     self.print(f'Agent {agent_state.i} did just clean up some dirt at {agent_state.pos}.')
                     info_dict.update(dirt_cleaned=1)
                 else:
-                    reward -= 0.0
+                    reward -= 0.01
                     self.print(f'Agent {agent_state.i} just tried to clean up some dirt '
                                f'at {agent_state.pos}, but was unsucsessfull.')
                     info_dict.update(failed_cleanup_attempt=1)
@@ -162,14 +162,14 @@ class SimpleFactory(BaseFactory):
                 else:
                     # info_dict.update(collision=1)
                     # self.print('collision')
-                    reward -= 0.00
+                    reward -= 0.01
 
             else:
                 info_dict.update(no_op=1)
                 reward -= 0.00
 
             for entity in list_of_collisions:
-                info_dict.update({f'agent_{agent_state.i}_vs_{self._state_slices.by_name(entity)}': 1})
+                info_dict.update({f'agent_{agent_state.i}_vs_{entity}': 1})
 
         info_dict.update(dirt_amount=current_dirt_amount)
         info_dict.update(dirty_tile_count=dirty_tiles)

environments/logging/plotting.py

@@ -1,8 +1,6 @@
 import seaborn as sns
-
 from matplotlib import pyplot as plt
 
-
 PALETTE = 10 * (
     "#377eb8",
     "#4daf4a",

main.py

@@ -12,7 +12,6 @@ from environments.factory.simple_factory import DirtProperties, SimpleFactory
 from environments.helpers import IGNORED_DF_COLUMNS
 from environments.logging.monitor import MonitorCallback
 from environments.logging.plotting import prepare_plot
-from environments.logging.training import TraningMonitor
 
 warnings.filterwarnings('ignore', category=FutureWarning)
 warnings.filterwarnings('ignore', category=UserWarning)
@@ -55,7 +54,7 @@ if __name__ == '__main__':
         for seed in range(5):
 
             env = SimpleFactory(n_agents=1, dirt_properties=dirt_props, pomdp_radius=2, max_steps=400,
-                                allow_diagonal_movement=False, allow_no_op=False, verbose=True)
+                                allow_diagonal_movement=False, allow_no_op=False, verbose=False)
 
             model = modeL_type("MlpPolicy", env, verbose=1, seed=seed, device='cpu')
 
@@ -65,8 +64,7 @@ if __name__ == '__main__':
             out_path /= identifier
 
             callbacks = CallbackList(
-                [TraningMonitor(out_path / f'train_logging_{identifier}.csv'),
+                [MonitorCallback(env, filepath=out_path / f'monitor_{identifier}.pick', plotting=False)]
-                 MonitorCallback(env, filepath=out_path / f'monitor_{identifier}.pick', plotting=False)]
             )
 
             model.learn(total_timesteps=int(2e5), callback=callbacks)

reload_agent.py

@@ -14,13 +14,13 @@ warnings.filterwarnings('ignore', category=UserWarning)
 
 if __name__ == '__main__':
 
-    out_path = Path(r'C:\Users\steff\projects\f_iks\debug_out\A2C_1622557712')
+    out_path = Path(r'C:\Users\steff\projects\f_iks\debug_out\A2C_1622558379')
     with (out_path / f'env_{out_path.name}.pick').open('rb') as f:
         env_kwargs = pickle.load(f)
-    env = SimpleFactory(**env_kwargs)
+    env = SimpleFactory(allow_no_op=False, allow_diagonal_movement=False, allow_square_movement=True, **env_kwargs)
 
     # Edit THIS:
-    model_path = out_path
+    model_path = out_path / '1_A2C_1622558379'
 
     model_files = list(natsorted(out_path.rglob('*.zip')))
     this_model = model_files[0]