first steps to gym

dirt spawn frequency

environments/factory/simple_factory_getting_dirty.py

@@ -11,7 +11,6 @@ from environments import helpers as h
 from environments.factory.renderer import Renderer
 from environments.factory.renderer import Entity
 
-
 DIRT_INDEX = -1
 
 
@@ -40,7 +39,7 @@ class GettingDirty(BaseFactory):
             height, width = self.state.shape[1:]
             self.renderer = Renderer(width, height, view_radius=0)
 
-        dirt   = [Entity('dirt', [x, y], (min(self.state[DIRT_INDEX, x, y],1)), 'scale') for x, y in np.argwhere(self.state[DIRT_INDEX] > h.IS_FREE_CELL)]
+        dirt   = [Entity('dirt', [x, y], self.state[DIRT_INDEX, x, y]) for x, y in np.argwhere(self.state[DIRT_INDEX] > h.IS_FREE_CELL)]
         walls  = [Entity('dirt', pos) for pos in np.argwhere(self.state[h.LEVEL_IDX] > h.IS_FREE_CELL)]
         agents = [Entity('agent', pos) for pos in np.argwhere(self.state[h.AGENT_START_IDX] > h.IS_FREE_CELL)]
 
@@ -101,7 +100,10 @@ class GettingDirty(BaseFactory):
 
     def calculate_reward(self, agent_states: List[AgentState]) -> (int, dict):
         # TODO: What reward to use?
-        this_step_reward = 0
+        current_dirt_amount = self.state[DIRT_INDEX].sum()
+        dirty_tiles = len(np.nonzero(self.state[DIRT_INDEX]))
+
+        this_step_reward = -(dirty_tiles / current_dirt_amount)
 
         for agent_state in agent_states:
             collisions = agent_state.collisions
@@ -113,8 +115,8 @@ class GettingDirty(BaseFactory):
             for entity in collisions:
                 if entity != self.string_slices["dirt"]:
                     self.monitor.add(f'agent_{agent_state.i}_vs_{self.slice_strings[entity]}', 1)
-        self.monitor.set('dirt_amount', self.state[DIRT_INDEX].sum())
-        self.monitor.set('dirty_tiles', len(np.nonzero(self.state[DIRT_INDEX])))
+        self.monitor.set('dirt_amount', current_dirt_amount)
+        self.monitor.set('dirty_tiles', dirty_tiles)
         return this_step_reward, {}
 
 
@@ -126,13 +128,13 @@ if __name__ == '__main__':
     monitor_list = list()
     for epoch in range(100):
         random_actions = [random.randint(0, 8) for _ in range(200)]
-        state, r, done, _ = factory.reset()
-        for action in random_actions:
-            state, r, done, info = factory.step(action)
+        env_state, reward, done_bool, _ = factory.reset()
+        for agent_i_action in random_actions:
+            env_state, reward, done_bool, info_obj = factory.step(agent_i_action)
             if render:
                 factory.render()
         monitor_list.append(factory.monitor.to_pd_dataframe())
-        print(f'Factory run {epoch} done, reward is:\n    {r}')
+        print(f'Factory run {epoch} done, reward is:\n    {reward}')
 
     from pathlib import Path
     import pickle