Refactoring of Collision checking bug fixes

environments/factory/base_factory.py

@@ -1,4 +1,5 @@
 import random
+from typing import Tuple, List, Union, Iterable
 
 import numpy as np
 from pathlib import Path
@@ -10,6 +11,8 @@ class BaseFactory:
     def __init__(self, level='simple', n_agents=1, max_steps=1e3):
         self.n_agents = n_agents
         self.max_steps = max_steps
+        self.allow_vertical_movement = True
+        self.allow_horizontal_movement = True
         self.level = h.one_hot_level(
             h.parse_level(Path(__file__).parent / h.LEVELS_DIR / f'{level}.txt')
         )
@@ -38,49 +41,66 @@ class BaseFactory:
         actions = [actions] if isinstance(actions, int) else actions
         assert isinstance(actions, list), f'"actions has to be in [{int, list}]'
         self.steps += 1
+
+        # FixMe: Why do we need this?
         r = 0
 
+        # Move this in a seperate function?
         actions = list(enumerate(actions))
         random.shuffle(actions)
         for agent_i, action in actions:
-            if action <= 8:
+            if self._is_moving_action(action):
-                pos, did_collide = self.move_or_colide(agent_i, action)
+                pos, valid = self.move_or_colide(agent_i, action)
             else:
-                pos, did_collide = self.additional_actions(agent_i, action)
+                pos, valid = self.additional_actions(agent_i, action)
-            actions[agent_i] = (pos, did_collide)
+            actions[agent_i] = (agent_i, action, pos, valid)
 
-        collision_vecs = np.zeros((self.n_agents, self.state.shape[0]))  # n_agents x n_slices
+        collision_vecs = self.check_all_collisions(actions, self.state.shape[0])
-        for agent_i, action in enumerate(actions):
-            collision_vecs[agent_i] = self.check_collisions(agent_i, *action)
 
-        reward, info = self.step_core(collision_vecs, actions, r)
+        reward, info = self.calculate_reward(collision_vecs, [a[1] for a in actions], r)
         r += reward
         if self.steps >= self.max_steps:
             self.done = True
         return self.state, r, self.done, info
 
-    def check_collisions(self, agent_i, pos, valid):
+    def _is_moving_action(self, action):
+        movement_actions = (int(self.allow_vertical_movement) + int(self.allow_horizontal_movement)) * 4
+        if action < movement_actions:
+            return True
+        else:
+            return False
+
+    def check_all_collisions(self, agent_action_pos_valid_tuples: (int, int, (int, int), bool), collisions: int) -> np.ndarray:
+        collision_vecs = np.zeros((len(agent_action_pos_valid_tuples), collisions))  # n_agents x n_slices
+        for agent_i, action, pos, valid in agent_action_pos_valid_tuples:
+            if self._is_moving_action(action):
+                collision_vecs[agent_i] = self.check_collisions(agent_i, pos, valid)
+        return collision_vecs
+
+    def check_collisions(self, agent_i: int, pos: (int, int), valid: bool) -> np.ndarray:
         pos_x, pos_y = pos
-        collisions_vec = self.state[:, pos_x, pos_y].copy()  # "vertical fiber" at position of agent i
+        # FixMe: We need to find a way to spare out some dimensions, eg. an info dimension etc... a[?,]
+        collisions_vec = self.state[:, pos_x, pos_y].copy()           # "vertical fiber" at position of agent i
         collisions_vec[h.AGENT_START_IDX + agent_i] = h.IS_FREE_CELL  # no self-collisions
         if valid:
+            # ToDo: Place a function hook here
             pass
         else:
             collisions_vec[h.LEVEL_IDX] = h.IS_OCCUPIED_CELL
         return collisions_vec
 
-    def move(self, agent_i, old_pos, new_pos):
+    def do_move(self, agent_i: int, old_pos: (int, int), new_pos: (int, int)) -> None:
         (x, y), (x_new, y_new) = old_pos, new_pos
         self.state[agent_i + h.AGENT_START_IDX, x, y] = h.IS_FREE_CELL
         self.state[agent_i + h.AGENT_START_IDX, x_new, y_new] = h.IS_OCCUPIED_CELL
 
-    def move_or_colide(self, agent_i, action) -> ((int, int), bool):
+    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,
-                                                           dim=agent_i + h.AGENT_START_IDX,
+                                                     dim=agent_i + h.AGENT_START_IDX,
-                                                           action=action)
+                                                     action=action)
         if valid:
             # Does not collide width level boundrys
-            self.move(agent_i, old_pos, new_pos)
+            self.do_move(agent_i, old_pos, new_pos)
             return new_pos, valid
         else:
             # Agent seems to be trying to collide in this step
@@ -93,7 +113,7 @@ class BaseFactory:
         np.random.shuffle(free_cells)
         return free_cells
 
-    def step_core(self, collisions_vec, actions, r):
+    def calculate_reward(self, collisions_vec: np.ndarray, actions: Iterable[int], r: int) -> (int, dict):
         # Returns: Reward, Info
         # Set to "raise NotImplementedError"
-        return 0, {}  # What is returned here?
+        return 0, {}

environments/factory/simple_factory.py

@@ -19,7 +19,7 @@ class SimpleFactory(BaseFactory):
         self.state = np.concatenate((self.state, dirt_slice))  # dirt is now the last slice
         self.spawn_dirt()
 
-    def step_core(self, collisions_vecs, actions, r):
+    def calculate_reward(self, collisions_vecs, actions, r):
         for agent_i, cols in enumerate(collisions_vecs):
             cols = np.argwhere(cols != 0).flatten()
             print(f't = {self.steps}\tAgent {agent_i} has collisions with '

environments/factory/simple_factory_getting_dirty.py

@@ -30,7 +30,7 @@ class GettingDirty(BaseFactory):
         self.state = np.concatenate((self.state, dirt_slice))  # dirt is now the last slice
         self.spawn_dirt()
 
-    def step_core(self, collisions_vecs, actions, r):
+    def calculate_reward(self, collisions_vecs, actions, r):
         for agent_i, cols in enumerate(collisions_vecs):
             cols = np.argwhere(cols != 0).flatten()
             print(f't = {self.steps}\tAgent {agent_i} has collisions with '