43 lines
1.7 KiB
Python
43 lines
1.7 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 stable_baselines3 import PPO, DQN, A2C
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from stable_baselines3.common.evaluation import evaluate_policy
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from environments.factory.simple_factory import DirtProperties, SimpleFactory
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from environments.factory.double_task_factory import ItemProperties, DoubleTaskFactory
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warnings.filterwarnings('ignore', category=FutureWarning)
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warnings.filterwarnings('ignore', category=UserWarning)
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model_map = dict(PPO=PPO, DQN=DQN, A2C=A2C)
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if __name__ == '__main__':
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model_name = 'A2C_1630414444'
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run_id = 0
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seed=69
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out_path = Path(__file__).parent / 'debug_out'
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model_path = out_path / model_name
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with (model_path / f'env_{model_name}.yaml').open('r') as f:
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env_kwargs = yaml.load(f, Loader=yaml.FullLoader)
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env_kwargs.update(verbose=True, env_seed=seed)
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if False:
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env_kwargs.update(dirt_properties=DirtProperties(clean_amount=1, gain_amount=0.1, max_global_amount=20,
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max_local_amount=1, spawn_frequency=5, max_spawn_ratio=0.05,
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dirt_smear_amount=0.5),
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combin_agent_slices_in_obs=True, omit_agent_slice_in_obs=True)
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with SimpleFactory(**env_kwargs) as env:
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# Edit THIS:
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env.seed(seed)
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model_files = list(natsorted((model_path / f'{run_id}_{model_name}').rglob('model_*.zip')))
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this_model = model_files[0]
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model_cls = next(val for key, val in model_map.items() if key in model_name)
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model = model_cls.load(this_model)
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evaluation_result = evaluate_policy(model, env, n_eval_episodes=100, deterministic=True, render=True)
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print(evaluation_result)
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