Experiments look good

reload_agent.py

@@ -4,7 +4,10 @@ from pathlib import Path
 import yaml
 from stable_baselines3 import A2C, PPO, DQN
 
+from environments.factory.factory_dirt import Constants as c
+
 from environments.factory.factory_dirt import DirtFactory
+from environments.logging.envmonitor import EnvMonitor
 from environments.logging.recorder import EnvRecorder
 
 warnings.filterwarnings('ignore', category=FutureWarning)
@@ -16,32 +19,35 @@ if __name__ == '__main__':
     determin = False
     render = True
     record = False
-    seed = 67
+    verbose = True
+    seed = 13
     n_agents = 1
     # out_path = Path('study_out/e_1_new_reward/no_obs/dirt/A2C_new_reward/0_A2C_new_reward')
-    out_path = Path('study_out/test/dirt')
+    out_path = Path('study_out/reload')
     model_path = out_path
 
     with (out_path / f'env_params.json').open('r') as f:
         env_kwargs = yaml.load(f, Loader=yaml.FullLoader)
-        env_kwargs.update(additional_agent_placeholder=None, n_agents=n_agents, max_steps=150)
-        if gain_amount := env_kwargs.get('dirt_prop', {}).get('gain_amount', None):
-            env_kwargs['dirt_prop']['max_spawn_amount'] = gain_amount
-            del env_kwargs['dirt_prop']['gain_amount']
-
-        env_kwargs.update(record_episodes=record, done_at_collision=True)
+        env_kwargs.update(n_agents=n_agents, done_at_collision=False, verbose=verbose)
 
     this_model = out_path / 'model.zip'
 
     model_cls = PPO  # next(val for key, val in h.MODEL_MAP.items() if key in out_path.parent.name)
     models = [model_cls.load(this_model)]
+    try:
+        # Legacy Cleanups
+        del env_kwargs['dirt_prop']['agent_can_interact']
+        env_kwargs['verbose'] = True
+    except KeyError:
+        pass
 
     # Init Env
     with DirtFactory(**env_kwargs) as env:
-        env = EnvRecorder(env)
+        env = EnvMonitor(env)
+        env = EnvRecorder(env) if record else env
         obs_shape = env.observation_space.shape
         # Evaluation Loop for i in range(n Episodes)
-        for episode in range(50):
+        for episode in range(500):
             env_state = env.reset()
             rew, done_bool = 0, False
             while not done_bool:
@@ -55,7 +61,17 @@ if __name__ == '__main__':
                 rew += step_r
                 if render:
                     env.render()
+                try:
+                    door = next(x for x in env.unwrapped.unwrapped[c.DOORS] if x.is_open)
+                    print('openDoor found')
+                except StopIteration:
+                    pass
+
                 if done_bool:
                     break
-            print(f'Factory run {episode} done, reward is:\n    {rew}')
+            print(f'Factory run {episode} done, steps taken {env.unwrapped.unwrapped._steps}, reward is:\n    {rew}')
+        env.save_run(out_path / 'reload_monitor.pick',
+                     auto_plotting_keys=['step_reward', 'cleanup_valid', 'cleanup_fail'])
+        if record:
+            env.save_records(out_path / 'reload_recorder.pick', save_occupation_map=True)
     print('all done')