63 lines
2.1 KiB
Python
63 lines
2.1 KiB
Python
import warnings
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from pathlib import Path
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import numpy as np
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import yaml
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from environments import helpers as h
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from environments.helpers import Constants as c
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from environments.factory.factory_dirt import DirtFactory
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from environments.factory.combined_factories import DirtItemFactory
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from environments.logging.recorder import EnvRecorder
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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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determin = False
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render = True
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record = True
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seed = 67
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n_agents = 1
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out_path = Path('study_out/test/dirt')
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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(additional_agent_placeholder=None, n_agents=n_agents, max_steps=150)
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if gain_amount := env_kwargs.get('dirt_prop', {}).get('gain_amount', None):
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env_kwargs['dirt_prop']['max_spawn_amount'] = gain_amount
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del env_kwargs['dirt_prop']['gain_amount']
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env_kwargs.update(record_episodes=record, done_at_collision=True)
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this_model = out_path / 'model.zip'
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model_cls =h.MODEL_MAP['A2C']
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models = [model_cls.load(this_model)]
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# Init Env
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with DirtFactory(**env_kwargs) as env:
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env = EnvRecorder(env)
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obs_shape = env.observation_space.shape
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# Evaluation Loop for i in range(n Episodes)
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for episode in range(50):
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env_state = env.reset()
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rew, done_bool = 0, False
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while not done_bool:
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if n_agents > 1:
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actions = [model.predict(env_state[model_idx], deterministic=True)[0]
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for model_idx, model in enumerate(models)]
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else:
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actions = models[0].predict(env_state, deterministic=determin)[0]
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env_state, step_r, done_bool, info_obj = env.step(actions)
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rew += step_r
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if render:
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env.render()
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if 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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