mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
8903bcd0c3
* Added tracing of eager policies with `tf.function` * lint * add config option * add docs * wip * tracing now works with a3c * typo * none * file doc * returns * syntax error * syntax error
57 lines
1.6 KiB
Python
57 lines
1.6 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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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("--iters", type=int, default=200)
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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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ray.init()
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args = parser.parse_args()
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tune.run(
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MyTrainer,
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stop={"training_iteration": args.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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