mirror of
https://github.com/vale981/ray
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73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
##########
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# Contribution by the Center on Long-Term Risk:
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# https://github.com/longtermrisk/marltoolbox
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##########
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import argparse
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import os
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import ray
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from ray import tune
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from ray.rllib.agents.pg import PGTrainer
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from ray.rllib.examples.env.matrix_sequential_social_dilemma import \
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IteratedPrisonersDilemma
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parser = argparse.ArgumentParser()
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parser.add_argument("--tf", action="store_true")
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parser.add_argument("--stop-iters", type=int, default=200)
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def main(debug, stop_iters=200, tf=False):
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train_n_replicates = 1 if debug else 1
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seeds = list(range(train_n_replicates))
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ray.init(num_cpus=os.cpu_count(), num_gpus=0, local_mode=debug)
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rllib_config, stop_config = get_rllib_config(seeds, debug, stop_iters, tf)
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tune_analysis = tune.run(
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PGTrainer,
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config=rllib_config,
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stop=stop_config,
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checkpoint_freq=0,
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checkpoint_at_end=True,
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name="PG_IPD")
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ray.shutdown()
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return tune_analysis
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def get_rllib_config(seeds, debug=False, stop_iters=200, tf=False):
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stop_config = {
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"training_iteration": 2 if debug else stop_iters,
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}
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env_config = {
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"players_ids": ["player_row", "player_col"],
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"max_steps": 20,
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"get_additional_info": True,
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}
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rllib_config = {
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"env": IteratedPrisonersDilemma,
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"env_config": env_config,
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"multiagent": {
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"policies": {
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env_config["players_ids"][0]: (
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None, IteratedPrisonersDilemma.OBSERVATION_SPACE,
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IteratedPrisonersDilemma.ACTION_SPACE, {}),
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env_config["players_ids"][1]: (
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None, IteratedPrisonersDilemma.OBSERVATION_SPACE,
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IteratedPrisonersDilemma.ACTION_SPACE, {}),
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},
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"policy_mapping_fn": lambda agent_id: agent_id,
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},
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"seed": tune.grid_search(seeds),
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"num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
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"framework": "tf" if tf else "torch",
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}
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return rllib_config, stop_config
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if __name__ == "__main__":
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debug_mode = True
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args = parser.parse_args()
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main(debug_mode, args.stop_iters, args.tf)
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