actions are now checked by string

environments/factory/base_factory.py

@@ -26,22 +26,6 @@ class Entity():
     def __init__(self, pos):
         self._pos = pos
 
-    def check_agent_move(state: np.ndarray, dim: int, action: str):
-        agent_slice = state[dim]  # horizontal slice from state tensor
-        agent_pos = np.argwhere(agent_slice == 1)
-        if len(agent_pos) > 1:
-            raise AssertionError('Only one agent per slice is allowed.')
-        x, y = agent_pos[0]
-
-        # Actions
-        x_diff, y_diff = ACTIONMAP[action]
-        x_new = x + x_diff
-        y_new = y + y_diff
-
-
-
-        return (x, y), (x_new, y_new), valid
-
 
 class AgentState:
 
@@ -312,9 +296,7 @@ class BaseFactory(gym.Env):
         self._state[agent_i + h.AGENT_START_IDX, x_new, y_new] = h.IS_OCCUPIED_CELL
 
     def move_or_colide(self, agent_i: int, action: int) -> ((int, int), bool):
-        old_pos, new_pos, valid = h.check_agent_move(state=self._state,
+        old_pos, new_pos, valid = self._check_agent_move(agent_i=agent_i, action=self._actions[action])
-                                                     dim=agent_i + h.AGENT_START_IDX,
-                                                     action=action)
         if valid:
             # Does not collide width level boundaries
             self.do_move(agent_i, old_pos, new_pos)
@@ -323,6 +305,22 @@ class BaseFactory(gym.Env):
             # Agent seems to be trying to collide in this step
             return old_pos, valid
 
+    def _check_agent_move(self, agent_i, action: str):
+        agent_slice = self._state[h.AGENT_START_IDX + agent_i]  # horizontal slice from state tensor
+        agent_pos = np.argwhere(agent_slice == 1)
+        if len(agent_pos) > 1:
+            raise AssertionError('Only one agent per slice is allowed.')
+        x, y = agent_pos[0]
+
+        # Actions
+        x_diff, y_diff = h.ACTIONMAP[action]
+        x_new = x + x_diff
+        y_new = y + y_diff
+
+        valid = h.check_position(self._state[h.LEVEL_IDX], (x_new, y_new))
+
+        return (x, y), (x_new, y_new), valid
+
     def agent_i_position(self, agent_i: int) -> (int, int):
         positions = np.argwhere(self._state[h.AGENT_START_IDX + agent_i] == h.IS_OCCUPIED_CELL)
         assert positions.shape[0] == 1

environments/helpers.py

@@ -38,19 +38,19 @@ def one_hot_level(level, wall_char=WALL):
     return binary_grid
 
 
-def check_position(state: np.ndarray, position_to_check: Tuple[int, int], dim: int = 0):
+def check_position(slice_to_check_against: np.ndarray, position_to_check: Tuple[int, int]):
-    x, y = position_to_check
+    x_pos, y_pos = position_to_check
-    agent_slice = state[dim]
 
     # Check if agent colides with grid boundrys
     valid = not (
-            x < 0 or y < 0
+            x_pos < 0 or y_pos < 0
-            or x >= agent_slice.shape[0]
+            or x_pos >= slice_to_check_against.shape[0]
-            or y >= agent_slice.shape[0]
+            or y_pos >= slice_to_check_against.shape[0]
     )
 
     # Check for collision with level walls
-    valid = valid and not state[LEVEL_IDX][x, y]
+    valid = valid and not slice_to_check_against[x_pos, y_pos]
+    return valid
 
 
 if __name__ == '__main__':

main.py

@@ -85,8 +85,8 @@ def compare_runs(run_path: Path, run_identifier: int, parameter: Union[str, List
 
 if __name__ == '__main__':
 
-    compare_runs(Path('debug_out'), 1623052687, ['step_reward'])
+    # compare_runs(Path('debug_out'), 1623052687, ['step_reward'])
-    exit()
+    # exit()
 
     from stable_baselines3 import PPO, DQN, A2C
     from algorithms.reg_dqn import RegDQN