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https://github.com/vale981/ray
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57544b1ff9
- This PR completes any previously missing PyTorch Model counterparts to TFModels in examples/models. - It also makes sure, all example scripts in the rllib/examples folder are tested for both frameworks and learn the given task (this is often currently not checked) using a --as-test flag in connection with a --stop-reward.
54 lines
1.6 KiB
Python
54 lines
1.6 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.evaluation.postprocessing import discount
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from ray.rllib.policy.tf_policy_template import build_tf_policy
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from ray.rllib.utils import try_import_tf
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tf = try_import_tf()
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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.from_batch(train_batch)
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action_dist = dist_class(logits, model)
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return -tf.reduce_mean(
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action_dist.logp(train_batch["actions"]) * train_batch["returns"])
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def calculate_advantages(policy,
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sample_batch,
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other_agent_batches=None,
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episode=None):
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sample_batch["returns"] = discount(sample_batch["rewards"], 0.99)
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return sample_batch
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# <class 'ray.rllib.policy.tf_policy_template.MyTFPolicy'>
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MyTFPolicy = build_tf_policy(
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name="MyTFPolicy",
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loss_fn=policy_gradient_loss,
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postprocess_fn=calculate_advantages,
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)
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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=MyTFPolicy,
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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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})
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