49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
import warnings
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from pathlib import Path
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import yaml
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from natsort import natsorted
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from environments import helpers as h
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from environments.factory.factory_dirt_item import DirtItemFactory
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from environments.logging.recorder import RecorderCallback
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warnings.filterwarnings('ignore', category=FutureWarning)
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warnings.filterwarnings('ignore', category=UserWarning)
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if __name__ == '__main__':
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model_name = 'PPO_1631187073'
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run_id = 0
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seed = 69
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out_path = Path(__file__).parent / 'study_out' / 'e_1_1631709932'/ 'no_obs' / 'itemdirt'/'A2C_1631709932' / '0_A2C_1631709932'
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model_path = out_path / model_name
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with (out_path / f'env_params.json').open('r') as f:
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env_kwargs = yaml.load(f, Loader=yaml.FullLoader)
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env_kwargs.update(verbose=False, env_seed=seed, record_episodes=True)
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this_model = out_path / 'model.zip'
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model_cls = next(val for key, val in h.MODEL_MAP.items() if key in model_name)
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model = model_cls.load(this_model)
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with RecorderCallback(filepath=Path() / 'recorder_out.json') as recorder:
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# Init Env
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with DirtItemFactory(**env_kwargs) as env:
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# Evaluation Loop for i in range(n Episodes)
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for episode in range(5):
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obs = env.reset()
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rew, done_bool = 0, False
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while not done_bool:
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action = model.predict(obs, deterministic=False)[0]
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env_state, step_r, done_bool, info_obj = env.step(action[0])
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recorder.read_info(0, info_obj)
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rew += step_r
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if done_bool:
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recorder.read_done(0, done_bool)
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break
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print(f'Factory run {episode} done, reward is:\n {rew}')
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print('all done')
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