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https://github.com/vale981/ray
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41 lines
1.3 KiB
Python
41 lines
1.3 KiB
Python
import argparse
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import ray
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from ray import tune
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from ray.rllib.agents.trainer_template import build_trainer
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from ray.rllib.policy.sample_batch import SampleBatch
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from ray.rllib.policy.torch_policy_template import build_torch_policy
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parser = argparse.ArgumentParser()
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parser.add_argument("--stop-iters", type=int, default=200)
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parser.add_argument("--num-cpus", type=int, default=0)
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def policy_gradient_loss(policy, model, dist_class, train_batch):
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logits, _ = model({SampleBatch.CUR_OBS: train_batch[SampleBatch.CUR_OBS]})
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action_dist = dist_class(logits, model)
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log_probs = action_dist.logp(train_batch[SampleBatch.ACTIONS])
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return -train_batch[SampleBatch.REWARDS].dot(log_probs)
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# <class 'ray.rllib.policy.torch_policy_template.MyTorchPolicy'>
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MyTorchPolicy = build_torch_policy(
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name="MyTorchPolicy", loss_fn=policy_gradient_loss)
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# <class 'ray.rllib.agents.trainer_template.MyCustomTrainer'>
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MyTrainer = build_trainer(
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name="MyCustomTrainer",
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default_policy=MyTorchPolicy,
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)
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if __name__ == "__main__":
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args = parser.parse_args()
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ray.init(num_cpus=args.num_cpus or None)
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tune.run(
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MyTrainer,
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stop={"training_iteration": args.stop_iters},
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config={
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"env": "CartPole-v0",
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"num_workers": 2,
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"framework": "torch",
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})
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