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https://github.com/vale981/ray
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fcf897ee72
update rllib example to use Tuner API. Signed-off-by: xwjiang2010 <xwjiang2010@gmail.com>
37 lines
1.1 KiB
Python
37 lines
1.1 KiB
Python
from ray import air, tune
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from ray.tune.registry import register_env
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from ray.rllib.env.wrappers.pettingzoo_env import PettingZooEnv
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from pettingzoo.sisl import waterworld_v3
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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_v3.env())
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env = env_creator({})
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register_env("waterworld", env_creator)
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tune.Tuner(
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"APEX_DDPG",
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run_config=air.RunConfig(
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stop={"episodes_total": 60000},
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checkpoint_config=air.CheckpointConfig(
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checkpoint_frequency=10,
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),
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),
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param_space={
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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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# Method specific
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"multiagent": {
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"policies": set(env.agents),
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"policy_mapping_fn": (lambda agent_id, episode, **kwargs: agent_id),
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},
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},
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).fit()
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