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https://github.com/vale981/ray
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31 lines
1.1 KiB
Python
31 lines
1.1 KiB
Python
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import ray
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from ray.rllib.evaluation.postprocessing import Postprocessing, \
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compute_advantages
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from ray.rllib.policy.tf_policy_template import build_tf_policy
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from ray.rllib.policy.sample_batch import SampleBatch
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from ray.rllib.utils import try_import_tf
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tf = try_import_tf()
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def post_process_advantages(policy, sample_batch, other_agent_batches=None,
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episode=None):
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"""This adds the "advantages" column to the sample train_batch."""
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return compute_advantages(sample_batch, 0.0, policy.config["gamma"],
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use_gae=False)
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def pg_tf_loss(policy, model, dist_class, train_batch):
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"""The basic policy gradients loss."""
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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(action_dist.logp(train_batch[SampleBatch.ACTIONS])
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* train_batch[Postprocessing.ADVANTAGES])
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PGTFPolicy = build_tf_policy(
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name="PGTFPolicy",
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get_default_config=lambda: ray.rllib.agents.pg.pg.DEFAULT_CONFIG,
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postprocess_fn=post_process_advantages,
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loss_fn=pg_tf_loss)
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