mirror of
https://github.com/vale981/ray
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57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
from ray import tune
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from ray.tune.registry import register_env
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from ray.rllib.env.pettingzoo_env import PettingZooEnv
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from pettingzoo.sisl import waterworld_v0
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# Based on code from github.com/parametersharingmadrl/parametersharingmadrl
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if __name__ == "__main__":
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# RDQN - Rainbow DQN
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# ADQN - Apex DQN
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def env_creator(args):
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return PettingZooEnv(waterworld_v0.env())
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env = env_creator({})
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register_env("waterworld", env_creator)
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obs_space = env.observation_space
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act_space = env.action_space
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policies = {"shared_policy": (None, obs_space, act_space, {})}
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# for all methods
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policy_ids = list(policies.keys())
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tune.run(
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"APEX_DDPG",
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stop={"episodes_total": 60000},
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checkpoint_freq=10,
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config={
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# Enviroment specific
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"env": "waterworld",
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# General
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"num_gpus": 1,
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"num_workers": 2,
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"num_envs_per_worker": 8,
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"learning_starts": 1000,
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"buffer_size": int(1e5),
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"compress_observations": True,
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"rollout_fragment_length": 20,
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"train_batch_size": 512,
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"gamma": .99,
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"n_step": 3,
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"lr": .0001,
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"prioritized_replay_alpha": 0.5,
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"final_prioritized_replay_beta": 1.0,
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"target_network_update_freq": 50000,
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"timesteps_per_iteration": 25000,
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# Method specific
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"multiagent": {
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"policies": policies,
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"policy_mapping_fn": (lambda agent_id: "shared_policy"),
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},
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},
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)
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