mirror of
https://github.com/illiumst/marl-factory-grid.git
synced 2025-05-22 14:56:43 +02:00
68 lines
1.9 KiB
Python
68 lines
1.9 KiB
Python
from pathlib import Path
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from random import randint
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from tqdm import trange
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from marl_factory_grid.environment.factory import Factory
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from marl_factory_grid.utils.logging.envmonitor import EnvMonitor
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from marl_factory_grid.utils.logging.recorder import EnvRecorder
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from marl_factory_grid.utils.plotting.plot_single_runs import plot_single_run
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from marl_factory_grid.utils.tools import ConfigExplainer
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if __name__ == '__main__':
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# Render at each step?
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render = False
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# Reveal all possible Modules (Entities, Rules, Agents[Actions, Observations], etc.)
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explain_config = True
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# Collect statistics?
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monitor = True
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# Record as Protobuf?
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record = False
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# Plot Results?
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plotting = True
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run_path = Path('study_out')
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if explain_config:
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ce = ConfigExplainer()
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ce.save_all(run_path / 'all_available_configs.yaml')
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# Path to config File
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path = Path('marl_factory_grid/configs/eight_puzzle.yaml')
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# Env Init
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factory = Factory(path)
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# Record and Monitor
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if monitor:
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factory = EnvMonitor(factory)
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if record:
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factory = EnvRecorder(factory)
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# RL learn Loop
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for episode in trange(10):
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_ = factory.reset()
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done = False
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if render:
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factory.render()
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action_spaces = factory.action_space
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while not done:
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a = [randint(0, x.n - 1) for x in action_spaces]
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obs_type, _, reward, done, info = factory.step(a)
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if render:
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factory.render()
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if done:
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print(f'Episode {episode} done...')
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break
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if monitor:
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factory.save_monitor(run_path / 'test_monitor.pkl')
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if record:
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factory.save_records(run_path / 'test.pb')
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if plotting:
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factory.report_possible_colum_keys()
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plot_single_run(run_path, column_keys=['step_reward'])
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print('Done!!! Goodbye....')
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