ray/rllib/policy/torch_policy_template.py

import gym
from typing import Callable, Dict, List, Optional, Tuple, Type, Union

from ray.rllib.models.modelv2 import ModelV2
from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
from ray.rllib.policy.policy import Policy
from ray.rllib.policy.policy_template import build_policy_class
from ray.rllib.policy.sample_batch import SampleBatch
from ray.rllib.policy.torch_policy import TorchPolicy
from ray.rllib.utils.annotations import Deprecated
from ray.rllib.utils.framework import try_import_torch
from ray.rllib.utils.typing import ModelGradients, TensorType, \
    TrainerConfigDict

torch, _ = try_import_torch()


@Deprecated(new="build_policy_class(framework='torch')", error=False)
def build_torch_policy(
        name: str,
        *,
        loss_fn: Optional[Callable[[
            Policy, ModelV2, Type[TorchDistributionWrapper], SampleBatch
        ], Union[TensorType, List[TensorType]]]],
        get_default_config: Optional[Callable[[], TrainerConfigDict]] = None,
        stats_fn: Optional[Callable[[Policy, SampleBatch], Dict[
            str, TensorType]]] = None,
        postprocess_fn=None,
        extra_action_out_fn: Optional[Callable[[
            Policy, Dict[str, TensorType], List[TensorType], ModelV2,
            TorchDistributionWrapper
        ], Dict[str, TensorType]]] = None,
        extra_grad_process_fn: Optional[Callable[[
            Policy, "torch.optim.Optimizer", TensorType
        ], Dict[str, TensorType]]] = None,
        extra_learn_fetches_fn: Optional[Callable[[Policy], Dict[
            str, TensorType]]] = None,
        optimizer_fn: Optional[Callable[[Policy, TrainerConfigDict],
                                        "torch.optim.Optimizer"]] = None,
        validate_spaces: Optional[Callable[
            [Policy, gym.Space, gym.Space, TrainerConfigDict], None]] = None,
        before_init: Optional[Callable[
            [Policy, gym.Space, gym.Space, TrainerConfigDict], None]] = None,
        before_loss_init: Optional[Callable[[
            Policy, gym.spaces.Space, gym.spaces.Space, TrainerConfigDict
        ], None]] = None,
        after_init: Optional[Callable[
            [Policy, gym.Space, gym.Space, TrainerConfigDict], None]] = None,
        _after_loss_init: Optional[Callable[[
            Policy, gym.spaces.Space, gym.spaces.Space, TrainerConfigDict
        ], None]] = None,
        action_sampler_fn: Optional[Callable[[TensorType, List[
            TensorType]], Tuple[TensorType, TensorType]]] = None,
        action_distribution_fn: Optional[Callable[[
            Policy, ModelV2, TensorType, TensorType, TensorType
        ], Tuple[TensorType, type, List[TensorType]]]] = None,
        make_model: Optional[Callable[[
            Policy, gym.spaces.Space, gym.spaces.Space, TrainerConfigDict
        ], ModelV2]] = None,
        make_model_and_action_dist: Optional[Callable[[
            Policy, gym.spaces.Space, gym.spaces.Space, TrainerConfigDict
        ], Tuple[ModelV2, Type[TorchDistributionWrapper]]]] = None,
        compute_gradients_fn: Optional[Callable[[Policy, SampleBatch], Tuple[
            ModelGradients, dict]]] = None,
        apply_gradients_fn: Optional[Callable[
            [Policy, "torch.optim.Optimizer"], None]] = None,
        mixins: Optional[List[type]] = None,
        get_batch_divisibility_req: Optional[Callable[[Policy], int]] = None
) -> Type[TorchPolicy]:

    kwargs = locals().copy()
    # Set to torch and call new function.
    kwargs["framework"] = "torch"
    return build_policy_class(**kwargs)