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https://github.com/vale981/ray
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82 lines
2.6 KiB
Python
82 lines
2.6 KiB
Python
"""
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PyTorch policy class used for PG.
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"""
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from typing import Dict, 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.torch.torch_action_dist import TorchDistributionWrapper
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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.policy_template import build_policy_class
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from ray.rllib.policy.sample_batch import SampleBatch
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from ray.rllib.utils.framework import try_import_torch
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from ray.rllib.utils.typing import TensorType
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torch, _ = try_import_torch()
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def pg_torch_loss(
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policy: Policy, model: ModelV2,
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dist_class: Type[TorchDistributionWrapper],
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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.from_batch(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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log_probs = action_dist.logp(train_batch[SampleBatch.ACTIONS])
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# Save the loss in the policy object for the stats_fn below.
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policy.pi_err = -torch.mean(
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log_probs * train_batch[Postprocessing.ADVANTAGES])
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return policy.pi_err
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def pg_loss_stats(policy: Policy,
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train_batch: SampleBatch) -> Dict[str, TensorType]:
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"""Returns the calculated loss in a stats dict.
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Args:
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policy (Policy): The Policy object.
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train_batch (SampleBatch): The data used for training.
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Returns:
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Dict[str, TensorType]: The stats dict.
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"""
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return {
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# `pi_err` (the loss) is stored inside `pg_torch_loss()`.
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"policy_loss": policy.pi_err.item(),
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}
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# Build a child class of `TFPolicy`, 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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PGTorchPolicy = build_policy_class(
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name="PGTorchPolicy",
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framework="torch",
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get_default_config=lambda: ray.rllib.agents.pg.pg.DEFAULT_CONFIG,
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loss_fn=pg_torch_loss,
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stats_fn=pg_loss_stats,
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postprocess_fn=post_process_advantages,
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)
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