recoder adaption

reload_agent.py

@@ -3,10 +3,10 @@ from pathlib import Path
 
 import yaml
 from natsort import natsorted
-from stable_baselines3.common.evaluation import evaluate_policy
+from environments import helpers as h
 
-from environments.factory.factory_dirt import DirtProperties, DirtFactory
-from environments.factory.factory_item import ItemProperties, ItemFactory
+from environments.factory.factory_dirt_item import DirtItemFactory
+from environments.logging.recorder import RecorderCallback
 
 warnings.filterwarnings('ignore', category=FutureWarning)
 warnings.filterwarnings('ignore', category=UserWarning)
@@ -14,27 +14,35 @@ warnings.filterwarnings('ignore', category=UserWarning)
 
 if __name__ == '__main__':
 
-    model_name = 'DQN_1631092016'
+    model_name = 'PPO_1631187073'
     run_id = 0
     seed = 69
-    out_path = Path(__file__).parent / 'debug_out'
+    out_path = Path(__file__).parent / 'study_out' / 'e_1_1631709932'/ 'no_obs' / 'itemdirt'/'A2C_1631709932' / '0_A2C_1631709932'
     model_path = out_path / model_name
 
-    with (model_path / f'env_{model_name}.yaml').open('r') as f:
+    with (out_path / f'env_params.json').open('r') as f:
         env_kwargs = yaml.load(f, Loader=yaml.FullLoader)
-        env_kwargs.update(verbose=True, env_seed=seed)
-    if False:
-        env_kwargs.update(dirt_properties=DirtProperties(clean_amount=1, gain_amount=0.1, max_global_amount=20,
-                                                         max_local_amount=1, spawn_frequency=5, max_spawn_ratio=0.05,
-                                                         dirt_smear_amount=0.5),
-                          combin_agent_slices_in_obs=True, omit_agent_slice_in_obs=True)
-    with ItemFactory(**env_kwargs) as env:
+        env_kwargs.update(verbose=False, env_seed=seed, record_episodes=True)
 
-        # Edit THIS:
-        env.seed(seed)
-        model_files = list(natsorted((model_path / f'{run_id}_{model_name}').rglob('model_*.zip')))
-        this_model = model_files[0]
-        model_cls = next(val for key, val in model_map.items() if key in model_name)
-        model = model_cls.load(this_model)
-        evaluation_result = evaluate_policy(model, env, n_eval_episodes=100, deterministic=False, render=True)
-        print(evaluation_result)
+    this_model = out_path / 'model.zip'
+
+    model_cls = next(val for key, val in h.MODEL_MAP.items() if key in model_name)
+    model = model_cls.load(this_model)
+
+    with RecorderCallback(filepath=Path() / 'recorder_out.json') as recorder:
+        # Init Env
+        with DirtItemFactory(**env_kwargs) as env:
+            # Evaluation Loop for i in range(n Episodes)
+            for episode in range(5):
+                obs = env.reset()
+                rew, done_bool = 0, False
+                while not done_bool:
+                    action = model.predict(obs, deterministic=False)[0]
+                    env_state, step_r, done_bool, info_obj = env.step(action[0])
+                    recorder.read_info(0, info_obj)
+                    rew += step_r
+                    if done_bool:
+                        recorder.read_done(0, done_bool)
+                        break
+                print(f'Factory run {episode} done, reward is:\n    {rew}')
+    print('all done')