added simple example

environments/factory/factory_dirt.py

@@ -198,19 +198,19 @@ class DirtFactory(BaseFactory):
 
     def step_hook(self) -> (List[dict], dict):
         super_reward_info = super().step_hook()
-        if smear_amount := self.dirt_prop.dirt_smear_amount:
+        # if smear_amount := self.dirt_prop.dirt_smear_amount:
-            for agent in self[c.AGENT]:
+        #     for agent in self[c.AGENT]:
-                if agent.temp_valid and agent.last_pos != c.NO_POS:
+        #         if agent.temp_valid and agent.last_pos != c.NO_POS:
-                    if self._actions.is_moving_action(agent.temp_action):
+        #             if self._actions.is_moving_action(agent.temp_action):
-                        if old_pos_dirt := self[c.DIRT].by_pos(agent.last_pos):
+        #                 if old_pos_dirt := self[c.DIRT].by_pos(agent.last_pos):
-                            if smeared_dirt := round(old_pos_dirt.amount * smear_amount, 2):
+        #                     if smeared_dirt := round(old_pos_dirt.amount * smear_amount, 2):
-                                old_pos_dirt.set_new_amount(max(0, old_pos_dirt.amount-smeared_dirt))
+        #                         old_pos_dirt.set_new_amount(max(0, old_pos_dirt.amount-smeared_dirt))
-                                if new_pos_dirt := self[c.DIRT].by_pos(agent.pos):
+        #                         if new_pos_dirt := self[c.DIRT].by_pos(agent.pos):
-                                    new_pos_dirt.set_new_amount(max(0, new_pos_dirt.amount + smeared_dirt))
+        #                             new_pos_dirt.set_new_amount(max(0, new_pos_dirt.amount + smeared_dirt))
-                                else:
+        #                         else:
-                                    if self[c.DIRT].spawn_dirt(agent.tile):
+        #                             if self[c.DIRT].spawn_dirt(agent.tile):
-                                        new_pos_dirt = self[c.DIRT].by_pos(agent.pos)
+        #                                 new_pos_dirt = self[c.DIRT].by_pos(agent.pos)
-                                        new_pos_dirt.set_new_amount(max(0, new_pos_dirt.amount + smeared_dirt))
+        #                                 new_pos_dirt.set_new_amount(max(0, new_pos_dirt.amount + smeared_dirt))
         if self._next_dirt_spawn < 0:
             pass  # No Dirt Spawn
         elif not self._next_dirt_spawn:

environments/logging/envmonitor.py

@@ -47,7 +47,7 @@ class EnvMonitor(BaseCallback):
             self._read_info(env_idx, info)
 
         for env_idx, done in list(
-                enumerate(self.locals.get('dones', []))) + list(enumerate(self.locals.get('done', []))):
+                enumerate(self.locals.get('dones', []))): # + list(enumerate(self.locals.get('done', []))):
             self._read_done(env_idx, done)
         return True
 

experiments/simple_example.py

@@ -0,0 +1,119 @@
+import warnings
+from pathlib import Path
+
+import yaml
+
+from stable_baselines3 import PPO
+
+from environments.factory.factory_dirt import DirtProperties, DirtFactory, RewardsDirt
+from environments.logging.envmonitor import EnvMonitor
+from environments.logging.recorder import EnvRecorder
+from environments.utility_classes import MovementProperties, ObservationProperties, AgentRenderOptions
+from environments.factory.factory_dirt import Constants as c
+
+warnings.filterwarnings('ignore', category=FutureWarning)
+warnings.filterwarnings('ignore', category=UserWarning)
+
+if __name__ == '__main__':
+    TRAIN_AGENT = True
+    LOAD_AND_REPLAY = True
+    record = True
+    render = False
+
+    study_root_path = Path(__file__).parent.parent / 'experiment_out'
+
+    parameter_path = Path(__file__).parent.parent / 'environments' / 'factory' / 'levels' / 'parameters' / 'DirtyFactory-v0.yaml'
+
+    save_path = study_root_path / f'model.zip'
+
+    # Output folder
+
+    study_root_path.mkdir(parents=True, exist_ok=True)
+
+    train_steps = 2*1e5
+    frames_to_stack = 0
+
+    u = dict(
+        show_global_position_info=True,
+        pomdp_r=3,
+        cast_shadows=True,
+        allow_diagonal_movement=False,
+        parse_doors=True,
+        doors_have_area=False,
+        done_at_collision=True
+    )
+    obs_props = ObservationProperties(render_agents=AgentRenderOptions.SEPERATE,
+                                      additional_agent_placeholder=None,
+                                      omit_agent_self=True,
+                                      frames_to_stack=frames_to_stack,
+                                      pomdp_r=u['pomdp_r'], cast_shadows=u['cast_shadows'],
+                                      show_global_position_info=u['show_global_position_info'])
+    move_props = MovementProperties(allow_diagonal_movement=u['allow_diagonal_movement'],
+                                    allow_square_movement=True,
+                                    allow_no_op=False)
+    dirt_props = DirtProperties(initial_dirt_ratio=0.35, initial_dirt_spawn_r_var=0.1,
+                                clean_amount=0.34,
+                                max_spawn_amount=0.1, max_global_amount=20,
+                                max_local_amount=1, spawn_frequency=0, max_spawn_ratio=0.05,
+                                dirt_smear_amount=0.0)
+    rewards_dirt = RewardsDirt(CLEAN_UP_FAIL=-0.5, CLEAN_UP_VALID=1, CLEAN_UP_LAST_PIECE=5)
+    factory_kwargs = dict(n_agents=1, max_steps=500, parse_doors=u['parse_doors'],
+                          level_name='rooms', doors_have_area=u['doors_have_area'],
+                          verbose=True,
+                          mv_prop=move_props,
+                          obs_prop=obs_props,
+                          rewards_dirt=rewards_dirt,
+                          done_at_collision=u['done_at_collision']
+                          )
+
+    # with (parameter_path).open('r') as f:
+    #     factory_kwargs = yaml.load(f, Loader=yaml.FullLoader)
+    #     factory_kwargs.update(n_agents=1, done_at_collision=False, verbose=True)
+
+    if TRAIN_AGENT:
+        env = DirtFactory(**factory_kwargs)
+        callbacks = EnvMonitor(env)
+        obs_shape = env.observation_space.shape
+
+        model = PPO("MlpPolicy", env, verbose=1, device='cpu')
+
+        model.learn(total_timesteps=train_steps, callback=callbacks)
+
+        callbacks.save_run(study_root_path / 'monitor.pick', auto_plotting_keys=['step_reward', 'collision'] + ['cleanup_valid', 'cleanup_fail']) # + env_plot_keys)
+
+
+        model.save(save_path)
+
+    if LOAD_AND_REPLAY:
+        with DirtFactory(**factory_kwargs) as env:
+            env = EnvMonitor(env)
+            env = EnvRecorder(env) if record else env
+            obs_shape = env.observation_space.shape
+            model = PPO.load(save_path)
+            # Evaluation Loop for i in range(n Episodes)
+            for episode in range(10):
+                env_state = env.reset()
+                rew, done_bool = 0, False
+                while not done_bool:
+                    actions = model.predict(env_state, deterministic=True)[0]
+                    env_state, step_r, done_bool, info_obj = env.step(actions)
+
+                    rew += step_r
+
+                    if render:
+                        env.render()
+
+                    try:
+                        door = next(x for x in env.unwrapped.unwrapped.unwrapped[c.DOORS] if x.is_open)
+                        print('openDoor found')
+                    except StopIteration:
+                        pass
+
+                    if done_bool:
+                        break
+                print(
+                    f'Factory run {episode} done, steps taken {env.unwrapped.unwrapped.unwrapped._steps}, reward is:\n    {rew}')
+
+            env.save_records(study_root_path / 'reload_recorder.pick', save_occupation_map=False)
+            #env.save_run(study_root_path / 'reload_monitor.pick',
+            #             auto_plotting_keys=['step_reward', 'cleanup_valid', 'cleanup_fail'])