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56 lines
2 KiB
Python
56 lines
2 KiB
Python
"""
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TensorFlow policy class used for PG.
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"""
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from typing import List, Type, Union
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import ray
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from ray.rllib.agents.pg.utils import post_process_advantages
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from ray.rllib.evaluation.postprocessing import Postprocessing
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from ray.rllib.models.action_dist import ActionDistribution
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from ray.rllib.models.modelv2 import ModelV2
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from ray.rllib.policy import Policy
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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.framework import try_import_tf
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from ray.rllib.utils.typing import TensorType
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tf1, tf, tfv = try_import_tf()
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def pg_tf_loss(
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policy: Policy, model: ModelV2, dist_class: Type[ActionDistribution],
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train_batch: SampleBatch) -> Union[TensorType, List[TensorType]]:
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"""The basic policy gradients loss function.
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Args:
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policy (Policy): The Policy to calculate the loss for.
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model (ModelV2): The Model to calculate the loss for.
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dist_class (Type[ActionDistribution]: The action distr. class.
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train_batch (SampleBatch): The training data.
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Returns:
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Union[TensorType, List[TensorType]]: A single loss tensor or a list
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of loss tensors.
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"""
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# Pass the training data through our model to get distribution parameters.
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dist_inputs, _ = model(train_batch)
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# Create an action distribution object.
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action_dist = dist_class(dist_inputs, model)
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# Calculate the vanilla PG loss based on:
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# L = -E[ log(pi(a|s)) * A]
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return -tf.reduce_mean(
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action_dist.logp(train_batch[SampleBatch.ACTIONS]) * tf.cast(
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train_batch[Postprocessing.ADVANTAGES], dtype=tf.float32))
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# Build a child class of `DynamicTFPolicy`, given the extra options:
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# - trajectory post-processing function (to calculate advantages)
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# - PG loss function
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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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