mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
733 lines
29 KiB
Python
733 lines
29 KiB
Python
import functools
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import gym
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import logging
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import numpy as np
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import os
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import time
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import threading
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from typing import Callable, Dict, List, Optional, Tuple, Type, Union
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from ray.rllib.models.modelv2 import ModelV2
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from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
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from ray.rllib.models.torch.torch_action_dist import TorchDistributionWrapper
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from ray.rllib.policy.policy import Policy, LEARNER_STATS_KEY
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from ray.rllib.policy.sample_batch import SampleBatch
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from ray.rllib.policy.rnn_sequencing import pad_batch_to_sequences_of_same_size
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from ray.rllib.utils import force_list
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from ray.rllib.utils.annotations import override, DeveloperAPI
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from ray.rllib.utils.framework import try_import_torch
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from ray.rllib.utils.schedules import ConstantSchedule, PiecewiseSchedule
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from ray.rllib.utils.threading import with_lock
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from ray.rllib.utils.torch_ops import convert_to_non_torch_type, \
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convert_to_torch_tensor
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from ray.rllib.utils.tracking_dict import UsageTrackingDict
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from ray.rllib.utils.typing import ModelGradients, ModelWeights, TensorType, \
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TrainerConfigDict
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torch, _ = try_import_torch()
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logger = logging.getLogger(__name__)
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@DeveloperAPI
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class TorchPolicy(Policy):
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"""Template for a PyTorch policy and loss to use with RLlib.
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Attributes:
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observation_space (gym.Space): observation space of the policy.
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action_space (gym.Space): action space of the policy.
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config (dict): config of the policy.
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model (TorchModel): Torch model instance.
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dist_class (type): Torch action distribution class.
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"""
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@DeveloperAPI
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def __init__(
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self,
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observation_space: gym.spaces.Space,
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action_space: gym.spaces.Space,
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config: TrainerConfigDict,
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*,
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model: ModelV2,
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loss: Callable[[
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Policy, ModelV2, Type[TorchDistributionWrapper], SampleBatch
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], Union[TensorType, List[TensorType]]],
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action_distribution_class: Type[TorchDistributionWrapper],
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action_sampler_fn: Optional[Callable[[
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TensorType, List[TensorType]
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], Tuple[TensorType, TensorType]]] = None,
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action_distribution_fn: Optional[Callable[[
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Policy, ModelV2, TensorType, TensorType, TensorType
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], Tuple[TensorType, Type[TorchDistributionWrapper], List[
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TensorType]]]] = None,
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max_seq_len: int = 20,
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get_batch_divisibility_req: Optional[Callable[[Policy],
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int]] = None,
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):
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"""Build a policy from policy and loss torch modules.
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Note that model will be placed on GPU device if CUDA_VISIBLE_DEVICES
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is set. Only single GPU is supported for now.
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Args:
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observation_space (gym.spaces.Space): observation space of the
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policy.
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action_space (gym.spaces.Space): action space of the policy.
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config (TrainerConfigDict): The Policy config dict.
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model (ModelV2): PyTorch policy module. Given observations as
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input, this module must return a list of outputs where the
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first item is action logits, and the rest can be any value.
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loss (Callable[[Policy, ModelV2, Type[TorchDistributionWrapper],
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SampleBatch], Union[TensorType, List[TensorType]]]): Callable
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that returns a single scalar loss or a list of loss terms.
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action_distribution_class (Type[TorchDistributionWrapper]): Class
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for a torch action distribution.
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action_sampler_fn (Callable[[TensorType, List[TensorType]],
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Tuple[TensorType, TensorType]]): A callable returning a
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sampled action and its log-likelihood given Policy, ModelV2,
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input_dict, explore, timestep, and is_training.
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action_distribution_fn (Optional[Callable[[Policy, ModelV2,
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ModelInputDict, TensorType, TensorType],
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Tuple[TensorType, type, List[TensorType]]]]): A callable
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returning distribution inputs (parameters), a dist-class to
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generate an action distribution object from, and
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internal-state outputs (or an empty list if not applicable).
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Note: No Exploration hooks have to be called from within
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`action_distribution_fn`. It's should only perform a simple
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forward pass through some model.
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If None, pass inputs through `self.model()` to get distribution
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inputs.
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The callable takes as inputs: Policy, ModelV2, ModelInputDict,
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explore, timestep, is_training.
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max_seq_len (int): Max sequence length for LSTM training.
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get_batch_divisibility_req (Optional[Callable[[Policy], int]]]):
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Optional callable that returns the divisibility requirement
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for sample batches given the Policy.
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"""
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self.framework = "torch"
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super().__init__(observation_space, action_space, config)
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if torch.cuda.is_available():
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logger.info("TorchPolicy running on GPU.")
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self.device = torch.device("cuda")
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else:
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logger.info("TorchPolicy running on CPU.")
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self.device = torch.device("cpu")
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self.model = model.to(self.device)
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# Lock used for locking some methods on the object-level.
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# This prevents possible race conditions when calling the model
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# first, then its value function (e.g. in a loss function), in
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# between of which another model call is made (e.g. to compute an
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# action).
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self._lock = threading.RLock()
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self._state_inputs = self.model.get_initial_state()
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self._is_recurrent = len(self._state_inputs) > 0
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# Auto-update model's inference view requirements, if recurrent.
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self._update_model_view_requirements_from_init_state()
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# Combine view_requirements for Model and Policy.
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self.view_requirements.update(self.model.view_requirements)
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self.exploration = self._create_exploration()
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self.unwrapped_model = model # used to support DistributedDataParallel
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self._loss = loss
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self._optimizers = force_list(self.optimizer())
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self.dist_class = action_distribution_class
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self.action_sampler_fn = action_sampler_fn
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self.action_distribution_fn = action_distribution_fn
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# If set, means we are using distributed allreduce during learning.
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self.distributed_world_size = None
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self.max_seq_len = max_seq_len
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self.batch_divisibility_req = get_batch_divisibility_req(self) if \
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callable(get_batch_divisibility_req) else \
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(get_batch_divisibility_req or 1)
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@override(Policy)
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@DeveloperAPI
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def compute_actions(
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self,
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obs_batch: Union[List[TensorType], TensorType],
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state_batches: Optional[List[TensorType]] = None,
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prev_action_batch: Union[List[TensorType], TensorType] = None,
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prev_reward_batch: Union[List[TensorType], TensorType] = None,
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info_batch: Optional[Dict[str, list]] = None,
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episodes: Optional[List["MultiAgentEpisode"]] = None,
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explore: Optional[bool] = None,
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timestep: Optional[int] = None,
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**kwargs) -> \
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Tuple[TensorType, List[TensorType], Dict[str, TensorType]]:
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with torch.no_grad():
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seq_lens = torch.ones(len(obs_batch), dtype=torch.int32)
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input_dict = self._lazy_tensor_dict({
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SampleBatch.CUR_OBS: np.asarray(obs_batch),
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"is_training": False,
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})
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if prev_action_batch is not None:
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input_dict[SampleBatch.PREV_ACTIONS] = \
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np.asarray(prev_action_batch)
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if prev_reward_batch is not None:
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input_dict[SampleBatch.PREV_REWARDS] = \
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np.asarray(prev_reward_batch)
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state_batches = [
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convert_to_torch_tensor(s, self.device)
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for s in (state_batches or [])
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]
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return self._compute_action_helper(input_dict, state_batches,
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seq_lens, explore, timestep)
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@override(Policy)
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def compute_actions_from_input_dict(
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self,
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input_dict: Dict[str, TensorType],
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explore: bool = None,
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timestep: Optional[int] = None,
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**kwargs) -> \
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Tuple[TensorType, List[TensorType], Dict[str, TensorType]]:
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with torch.no_grad():
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# Pass lazy (torch) tensor dict to Model as `input_dict`.
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input_dict = self._lazy_tensor_dict(input_dict)
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# Pack internal state inputs into (separate) list.
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state_batches = [
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input_dict[k] for k in input_dict.keys() if "state_in" in k[:8]
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]
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# Calculate RNN sequence lengths.
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seq_lens = np.array([1] * len(input_dict["obs"])) \
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if state_batches else None
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return self._compute_action_helper(input_dict, state_batches,
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seq_lens, explore, timestep)
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@with_lock
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def _compute_action_helper(self, input_dict, state_batches, seq_lens,
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explore, timestep):
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"""Shared forward pass logic (w/ and w/o trajectory view API).
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Returns:
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Tuple:
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- actions, state_out, extra_fetches, logp.
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"""
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explore = explore if explore is not None else self.config["explore"]
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timestep = timestep if timestep is not None else self.global_timestep
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self._is_recurrent = state_batches is not None and state_batches != []
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# Switch to eval mode.
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if self.model:
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self.model.eval()
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if self.action_sampler_fn:
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action_dist = dist_inputs = None
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actions, logp, state_out = self.action_sampler_fn(
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self,
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self.model,
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input_dict,
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state_batches,
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explore=explore,
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timestep=timestep)
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else:
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# Call the exploration before_compute_actions hook.
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self.exploration.before_compute_actions(
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explore=explore, timestep=timestep)
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if self.action_distribution_fn:
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# Try new action_distribution_fn signature, supporting
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# state_batches and seq_lens.
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try:
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dist_inputs, dist_class, state_out = \
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self.action_distribution_fn(
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self,
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self.model,
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input_dict=input_dict,
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state_batches=state_batches,
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seq_lens=seq_lens,
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explore=explore,
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timestep=timestep,
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is_training=False)
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# Trying the old way (to stay backward compatible).
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# TODO: Remove in future.
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except TypeError as e:
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if "positional argument" in e.args[0] or \
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"unexpected keyword argument" in e.args[0]:
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dist_inputs, dist_class, state_out = \
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self.action_distribution_fn(
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self,
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self.model,
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input_dict[SampleBatch.CUR_OBS],
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explore=explore,
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timestep=timestep,
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is_training=False)
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else:
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raise e
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else:
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dist_class = self.dist_class
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dist_inputs, state_out = self.model(input_dict, state_batches,
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seq_lens)
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if not (isinstance(dist_class, functools.partial)
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or issubclass(dist_class, TorchDistributionWrapper)):
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raise ValueError(
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"`dist_class` ({}) not a TorchDistributionWrapper "
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"subclass! Make sure your `action_distribution_fn` or "
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"`make_model_and_action_dist` return a correct "
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"distribution class.".format(dist_class.__name__))
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action_dist = dist_class(dist_inputs, self.model)
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# Get the exploration action from the forward results.
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actions, logp = \
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self.exploration.get_exploration_action(
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action_distribution=action_dist,
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timestep=timestep,
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explore=explore)
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input_dict[SampleBatch.ACTIONS] = actions
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# Add default and custom fetches.
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extra_fetches = self.extra_action_out(input_dict, state_batches,
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self.model, action_dist)
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# Action-dist inputs.
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if dist_inputs is not None:
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extra_fetches[SampleBatch.ACTION_DIST_INPUTS] = dist_inputs
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# Action-logp and action-prob.
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if logp is not None:
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extra_fetches[SampleBatch.ACTION_PROB] = \
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torch.exp(logp.float())
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extra_fetches[SampleBatch.ACTION_LOGP] = logp
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# Update our global timestep by the batch size.
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self.global_timestep += len(input_dict[SampleBatch.CUR_OBS])
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return convert_to_non_torch_type((actions, state_out, extra_fetches))
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@with_lock
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@override(Policy)
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@DeveloperAPI
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def compute_log_likelihoods(
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self,
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actions: Union[List[TensorType], TensorType],
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obs_batch: Union[List[TensorType], TensorType],
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state_batches: Optional[List[TensorType]] = None,
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prev_action_batch: Optional[Union[List[TensorType],
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TensorType]] = None,
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prev_reward_batch: Optional[Union[List[
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TensorType], TensorType]] = None) -> TensorType:
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if self.action_sampler_fn and self.action_distribution_fn is None:
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raise ValueError("Cannot compute log-prob/likelihood w/o an "
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"`action_distribution_fn` and a provided "
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"`action_sampler_fn`!")
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with torch.no_grad():
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input_dict = self._lazy_tensor_dict({
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SampleBatch.CUR_OBS: obs_batch,
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SampleBatch.ACTIONS: actions
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})
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if prev_action_batch is not None:
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input_dict[SampleBatch.PREV_ACTIONS] = prev_action_batch
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if prev_reward_batch is not None:
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input_dict[SampleBatch.PREV_REWARDS] = prev_reward_batch
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seq_lens = torch.ones(len(obs_batch), dtype=torch.int32)
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state_batches = [
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convert_to_torch_tensor(s, self.device)
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for s in (state_batches or [])
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]
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# Exploration hook before each forward pass.
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self.exploration.before_compute_actions(explore=False)
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# Action dist class and inputs are generated via custom function.
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if self.action_distribution_fn:
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dist_inputs, dist_class, _ = self.action_distribution_fn(
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policy=self,
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model=self.model,
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obs_batch=input_dict[SampleBatch.CUR_OBS],
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explore=False,
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is_training=False)
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# Default action-dist inputs calculation.
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else:
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dist_class = self.dist_class
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dist_inputs, _ = self.model(input_dict, state_batches,
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seq_lens)
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action_dist = dist_class(dist_inputs, self.model)
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log_likelihoods = action_dist.logp(input_dict[SampleBatch.ACTIONS])
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return log_likelihoods
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@with_lock
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@override(Policy)
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@DeveloperAPI
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def learn_on_batch(
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self, postprocessed_batch: SampleBatch) -> Dict[str, TensorType]:
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# Set Model to train mode.
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if self.model:
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self.model.train()
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# Callback handling.
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learn_stats = {}
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self.callbacks.on_learn_on_batch(
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policy=self, train_batch=postprocessed_batch, result=learn_stats)
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# Compute gradients (will calculate all losses and `backward()`
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# them to get the grads).
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grads, fetches = self.compute_gradients(postprocessed_batch)
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# Step the optimizers.
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for i, opt in enumerate(self._optimizers):
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opt.step()
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if self.model:
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fetches["model"] = self.model.metrics()
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fetches.update({"custom_metrics": learn_stats})
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return fetches
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|
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@with_lock
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@override(Policy)
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@DeveloperAPI
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def compute_gradients(self,
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postprocessed_batch: SampleBatch) -> ModelGradients:
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if not isinstance(postprocessed_batch, SampleBatch) or \
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not postprocessed_batch.zero_padded:
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pad_batch_to_sequences_of_same_size(
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postprocessed_batch,
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max_seq_len=self.max_seq_len,
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shuffle=False,
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batch_divisibility_req=self.batch_divisibility_req,
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view_requirements=self.view_requirements,
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)
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else:
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postprocessed_batch["seq_lens"] = postprocessed_batch.seq_lens
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# Mark the batch as "is_training" so the Model can use this
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# information.
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postprocessed_batch["is_training"] = True
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train_batch = self._lazy_tensor_dict(postprocessed_batch)
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# Calculate the actual policy loss.
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loss_out = force_list(
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self._loss(self, self.model, self.dist_class, train_batch))
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# Call Model's custom-loss with Policy loss outputs and train_batch.
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if self.model:
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loss_out = self.model.custom_loss(loss_out, train_batch)
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# Give Exploration component that chance to modify the loss (or add
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# its own terms).
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if hasattr(self, "exploration"):
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loss_out = self.exploration.get_exploration_loss(
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loss_out, train_batch)
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assert len(loss_out) == len(self._optimizers)
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# assert not any(torch.isnan(l) for l in loss_out)
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fetches = self.extra_compute_grad_fetches()
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|
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# Loop through all optimizers.
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grad_info = {"allreduce_latency": 0.0}
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all_grads = []
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for i, opt in enumerate(self._optimizers):
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# Erase gradients in all vars of this optimizer.
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opt.zero_grad()
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# Recompute gradients of loss over all variables.
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loss_out[i].backward(retain_graph=(i < len(self._optimizers) - 1))
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grad_info.update(self.extra_grad_process(opt, loss_out[i]))
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grads = []
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# Note that return values are just references;
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# Calling zero_grad would modify the values.
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for param_group in opt.param_groups:
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for p in param_group["params"]:
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if p.grad is not None:
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grads.append(p.grad)
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all_grads.append(p.grad.data.cpu().numpy())
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else:
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all_grads.append(None)
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if self.distributed_world_size:
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start = time.time()
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if torch.cuda.is_available():
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# Sadly, allreduce_coalesced does not work with CUDA yet.
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for g in grads:
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torch.distributed.all_reduce(
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g, op=torch.distributed.ReduceOp.SUM)
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else:
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torch.distributed.all_reduce_coalesced(
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grads, op=torch.distributed.ReduceOp.SUM)
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|
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for param_group in opt.param_groups:
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for p in param_group["params"]:
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if p.grad is not None:
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p.grad /= self.distributed_world_size
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grad_info["allreduce_latency"] += time.time() - start
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grad_info["allreduce_latency"] /= len(self._optimizers)
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grad_info.update(self.extra_grad_info(train_batch))
|
|
|
|
return all_grads, dict(fetches, **{LEARNER_STATS_KEY: grad_info})
|
|
|
|
@override(Policy)
|
|
@DeveloperAPI
|
|
def apply_gradients(self, gradients: ModelGradients) -> None:
|
|
# TODO(sven): Not supported for multiple optimizers yet.
|
|
assert len(self._optimizers) == 1
|
|
for g, p in zip(gradients, self.model.parameters()):
|
|
if g is not None:
|
|
p.grad = torch.from_numpy(g).to(self.device)
|
|
|
|
self._optimizers[0].step()
|
|
|
|
@override(Policy)
|
|
@DeveloperAPI
|
|
def get_weights(self) -> ModelWeights:
|
|
return {
|
|
k: v.cpu().detach().numpy()
|
|
for k, v in self.model.state_dict().items()
|
|
}
|
|
|
|
@override(Policy)
|
|
@DeveloperAPI
|
|
def set_weights(self, weights: ModelWeights) -> None:
|
|
weights = convert_to_torch_tensor(weights, device=self.device)
|
|
self.model.load_state_dict(weights)
|
|
|
|
@override(Policy)
|
|
@DeveloperAPI
|
|
def is_recurrent(self) -> bool:
|
|
return self._is_recurrent
|
|
|
|
@override(Policy)
|
|
@DeveloperAPI
|
|
def num_state_tensors(self) -> int:
|
|
return len(self.model.get_initial_state())
|
|
|
|
@override(Policy)
|
|
@DeveloperAPI
|
|
def get_initial_state(self) -> List[TensorType]:
|
|
return [
|
|
s.detach().cpu().numpy() for s in self.model.get_initial_state()
|
|
]
|
|
|
|
@override(Policy)
|
|
@DeveloperAPI
|
|
def get_state(self) -> Union[Dict[str, TensorType], List[TensorType]]:
|
|
state = super().get_state()
|
|
state["_optimizer_variables"] = []
|
|
for i, o in enumerate(self._optimizers):
|
|
optim_state_dict = convert_to_non_torch_type(o.state_dict())
|
|
state["_optimizer_variables"].append(optim_state_dict)
|
|
return state
|
|
|
|
@override(Policy)
|
|
@DeveloperAPI
|
|
def set_state(self, state: object) -> None:
|
|
state = state.copy() # shallow copy
|
|
# Set optimizer vars first.
|
|
optimizer_vars = state.pop("_optimizer_variables", None)
|
|
if optimizer_vars:
|
|
assert len(optimizer_vars) == len(self._optimizers)
|
|
for o, s in zip(self._optimizers, optimizer_vars):
|
|
optim_state_dict = convert_to_torch_tensor(
|
|
s, device=self.device)
|
|
o.load_state_dict(optim_state_dict)
|
|
# Then the Policy's (NN) weights.
|
|
super().set_state(state)
|
|
|
|
@DeveloperAPI
|
|
def extra_grad_process(self, optimizer: "torch.optim.Optimizer",
|
|
loss: TensorType):
|
|
"""Called after each optimizer.zero_grad() + loss.backward() call.
|
|
|
|
Called for each self._optimizers/loss-value pair.
|
|
Allows for gradient processing before optimizer.step() is called.
|
|
E.g. for gradient clipping.
|
|
|
|
Args:
|
|
optimizer (torch.optim.Optimizer): A torch optimizer object.
|
|
loss (TensorType): The loss tensor associated with the optimizer.
|
|
|
|
Returns:
|
|
Dict[str, TensorType]: An dict with information on the gradient
|
|
processing step.
|
|
"""
|
|
return {}
|
|
|
|
@DeveloperAPI
|
|
def extra_compute_grad_fetches(self) -> Dict[str, any]:
|
|
"""Extra values to fetch and return from compute_gradients().
|
|
|
|
Returns:
|
|
Dict[str, any]: Extra fetch dict to be added to the fetch dict
|
|
of the compute_gradients call.
|
|
"""
|
|
return {LEARNER_STATS_KEY: {}} # e.g, stats, td error, etc.
|
|
|
|
@DeveloperAPI
|
|
def extra_action_out(
|
|
self, input_dict: Dict[str, TensorType],
|
|
state_batches: List[TensorType], model: TorchModelV2,
|
|
action_dist: TorchDistributionWrapper) -> Dict[str, TensorType]:
|
|
"""Returns dict of extra info to include in experience batch.
|
|
|
|
Args:
|
|
input_dict (Dict[str, TensorType]): Dict of model input tensors.
|
|
state_batches (List[TensorType]): List of state tensors.
|
|
model (TorchModelV2): Reference to the model object.
|
|
action_dist (TorchDistributionWrapper): Torch action dist object
|
|
to get log-probs (e.g. for already sampled actions).
|
|
|
|
Returns:
|
|
Dict[str, TensorType]: Extra outputs to return in a
|
|
compute_actions() call (3rd return value).
|
|
"""
|
|
return {}
|
|
|
|
@DeveloperAPI
|
|
def extra_grad_info(self,
|
|
train_batch: SampleBatch) -> Dict[str, TensorType]:
|
|
"""Return dict of extra grad info.
|
|
|
|
Args:
|
|
train_batch (SampleBatch): The training batch for which to produce
|
|
extra grad info for.
|
|
|
|
Returns:
|
|
Dict[str, TensorType]: The info dict carrying grad info per str
|
|
key.
|
|
"""
|
|
return {}
|
|
|
|
@DeveloperAPI
|
|
def optimizer(
|
|
self
|
|
) -> Union[List["torch.optim.Optimizer"], "torch.optim.Optimizer"]:
|
|
"""Custom the local PyTorch optimizer(s) to use.
|
|
|
|
Returns:
|
|
Union[List[torch.optim.Optimizer], torch.optim.Optimizer]:
|
|
The local PyTorch optimizer(s) to use for this Policy.
|
|
"""
|
|
if hasattr(self, "config"):
|
|
return torch.optim.Adam(
|
|
self.model.parameters(), lr=self.config["lr"])
|
|
else:
|
|
return torch.optim.Adam(self.model.parameters())
|
|
|
|
@override(Policy)
|
|
@DeveloperAPI
|
|
def export_model(self, export_dir: str) -> None:
|
|
"""Exports the Policy's Model to local directory for serving.
|
|
|
|
Creates a TorchScript model and saves it.
|
|
|
|
Args:
|
|
export_dir (str): Local writable directory or filename.
|
|
"""
|
|
dummy_inputs = self._lazy_tensor_dict(self._dummy_batch.data)
|
|
# Provide dummy state inputs if not an RNN (torch cannot jit with
|
|
# returned empty internal states list).
|
|
if "state_in_0" not in dummy_inputs:
|
|
dummy_inputs["state_in_0"] = dummy_inputs["seq_lens"] = np.array(
|
|
[1.0])
|
|
state_ins = []
|
|
i = 0
|
|
while "state_in_{}".format(i) in dummy_inputs:
|
|
state_ins.append(dummy_inputs["state_in_{}".format(i)])
|
|
i += 1
|
|
seq_lens = dummy_inputs["seq_lens"]
|
|
dummy_inputs = {k: dummy_inputs[k] for k in dummy_inputs.keys()}
|
|
traced = torch.jit.trace(self.model,
|
|
(dummy_inputs, state_ins, seq_lens))
|
|
if not os.path.exists(export_dir):
|
|
os.makedirs(export_dir)
|
|
file_name = os.path.join(export_dir, "model.pt")
|
|
traced.save(file_name)
|
|
|
|
@override(Policy)
|
|
@DeveloperAPI
|
|
def export_checkpoint(self, export_dir: str) -> None:
|
|
"""TODO(sven): implement for torch.
|
|
"""
|
|
raise NotImplementedError
|
|
|
|
@override(Policy)
|
|
@DeveloperAPI
|
|
def import_model_from_h5(self, import_file: str) -> None:
|
|
"""Imports weights into torch model."""
|
|
return self.model.import_from_h5(import_file)
|
|
|
|
def _lazy_tensor_dict(self, batch):
|
|
if not isinstance(batch, UsageTrackingDict):
|
|
batch = UsageTrackingDict(batch)
|
|
batch.set_get_interceptor(
|
|
functools.partial(convert_to_torch_tensor, device=self.device))
|
|
return batch
|
|
|
|
def _lazy_numpy_dict(self, postprocessed_batch):
|
|
train_batch = UsageTrackingDict(postprocessed_batch)
|
|
train_batch.set_get_interceptor(
|
|
functools.partial(convert_to_non_torch_type))
|
|
return train_batch
|
|
|
|
|
|
# TODO: (sven) Unify hyperparam annealing procedures across RLlib (tf/torch)
|
|
# and for all possible hyperparams, not just lr.
|
|
@DeveloperAPI
|
|
class LearningRateSchedule:
|
|
"""Mixin for TFPolicy that adds a learning rate schedule."""
|
|
|
|
@DeveloperAPI
|
|
def __init__(self, lr, lr_schedule):
|
|
self.cur_lr = lr
|
|
if lr_schedule is None:
|
|
self.lr_schedule = ConstantSchedule(lr, framework=None)
|
|
else:
|
|
self.lr_schedule = PiecewiseSchedule(
|
|
lr_schedule, outside_value=lr_schedule[-1][-1], framework=None)
|
|
|
|
@override(Policy)
|
|
def on_global_var_update(self, global_vars):
|
|
super().on_global_var_update(global_vars)
|
|
self.cur_lr = self.lr_schedule.value(global_vars["timestep"])
|
|
for opt in self._optimizers:
|
|
for p in opt.param_groups:
|
|
p["lr"] = self.cur_lr
|
|
|
|
|
|
@DeveloperAPI
|
|
class EntropyCoeffSchedule:
|
|
"""Mixin for TorchPolicy that adds entropy coeff decay."""
|
|
|
|
@DeveloperAPI
|
|
def __init__(self, entropy_coeff, entropy_coeff_schedule):
|
|
self.entropy_coeff = entropy_coeff
|
|
|
|
if entropy_coeff_schedule is None:
|
|
self.entropy_coeff_schedule = ConstantSchedule(
|
|
entropy_coeff, framework=None)
|
|
else:
|
|
# Allows for custom schedule similar to lr_schedule format
|
|
if isinstance(entropy_coeff_schedule, list):
|
|
self.entropy_coeff_schedule = PiecewiseSchedule(
|
|
entropy_coeff_schedule,
|
|
outside_value=entropy_coeff_schedule[-1][-1],
|
|
framework=None)
|
|
else:
|
|
# Implements previous version but enforces outside_value
|
|
self.entropy_coeff_schedule = PiecewiseSchedule(
|
|
[[0, entropy_coeff], [entropy_coeff_schedule, 0.0]],
|
|
outside_value=0.0,
|
|
framework=None)
|
|
|
|
@override(Policy)
|
|
def on_global_var_update(self, global_vars):
|
|
super(EntropyCoeffSchedule, self).on_global_var_update(global_vars)
|
|
self.entropy_coeff = self.entropy_coeff_schedule.value(
|
|
global_vars["timestep"])
|