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Source code for torchrl.objectives.decision_transformer

# Copyright (c) Meta Platforms, Inc. and affiliates.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
from __future__ import annotations

import math
from dataclasses import dataclass
from typing import Union

import torch
from tensordict import TensorDict, TensorDictBase, TensorDictParams
from tensordict.nn import dispatch, TensorDictModule
from tensordict.utils import NestedKey

from torch import distributions as d
from torchrl.modules import ProbabilisticActor

from torchrl.objectives.common import LossModule
from torchrl.objectives.utils import _reduce, distance_loss


[docs]class OnlineDTLoss(LossModule): r"""TorchRL implementation of the Online Decision Transformer loss. Presented in `"Online Decision Transformer" <https://arxiv.org/abs/2202.05607>` Args: actor_network (ProbabilisticActor): stochastic actor Keyword Args: alpha_init (:obj:`float`, optional): initial entropy multiplier. Default is 1.0. min_alpha (:obj:`float`, optional): min value of alpha. Default is None (no minimum value). max_alpha (:obj:`float`, optional): max value of alpha. Default is None (no maximum value). fixed_alpha (bool, optional): if ``True``, alpha will be fixed to its initial value. Otherwise, alpha will be optimized to match the 'target_entropy' value. Default is ``False``. target_entropy (float or str, optional): Target entropy for the stochastic policy. Default is "auto", where target entropy is computed as :obj:`-prod(n_actions)`. samples_mc_entropy (int): number of samples to estimate the entropy reduction (str, optional): Specifies the reduction to apply to the output: ``"none"`` | ``"mean"`` | ``"sum"``. ``"none"``: no reduction will be applied, ``"mean"``: the sum of the output will be divided by the number of elements in the output, ``"sum"``: the output will be summed. Default: ``"mean"``. """ @dataclass class _AcceptedKeys: """Maintains default values for all configurable tensordict keys. This class defines which tensordict keys can be set using '.set_keys(key_name=key_value)' and their default values. Attributes: action_target (NestedKey): The input tensordict key where the action is expected. Defaults to ``"action"``. action_pred (NestedKey): The tensordict key where the output action (from the model) is expected. Used to compute the target entropy. Defaults to ``"action"``. """ # the "action" contained in the dataset action_target: NestedKey = "action" # the "action" output from the model action_pred: NestedKey = "action" default_keys = _AcceptedKeys() actor_network: TensorDictModule actor_network_params: TensorDictParams target_actor_network_params: TensorDictParams def __init__( self, actor_network: ProbabilisticActor, *, alpha_init: float = 1.0, min_alpha: float = None, max_alpha: float = None, fixed_alpha: bool = False, target_entropy: Union[str, float] = "auto", samples_mc_entropy: int = 1, reduction: str = None, ) -> None: self._in_keys = None self._out_keys = None if reduction is None: reduction = "mean" super().__init__() # Actor Network self.convert_to_functional( actor_network, "actor_network", create_target_params=False, ) try: device = next(self.parameters()).device except AttributeError: device = torch.device("cpu") self.register_buffer("alpha_init", torch.tensor(alpha_init, device=device)) if bool(min_alpha) ^ bool(max_alpha): min_alpha = min_alpha if min_alpha else 0.0 if max_alpha == 0: raise ValueError("max_alpha must be either None or greater than 0.") max_alpha = max_alpha if max_alpha else 1e9 if min_alpha: self.register_buffer( "min_log_alpha", torch.tensor(min_alpha, device=device).log() ) else: self.min_log_alpha = None if max_alpha: self.register_buffer( "max_log_alpha", torch.tensor(max_alpha, device=device).log() ) else: self.max_log_alpha = None self.fixed_alpha = fixed_alpha if fixed_alpha: self.register_buffer( "log_alpha", torch.tensor(math.log(alpha_init), device=device) ) else: self.register_parameter( "log_alpha", torch.nn.Parameter(torch.tensor(math.log(alpha_init), device=device)), ) if target_entropy == "auto": if actor_network.spec is None: raise RuntimeError( "Cannot infer the dimensionality of the action. Consider providing " "the target entropy explicitely or provide the spec of the " "action tensor in the actor network." ) if isinstance(self.tensor_keys.action_pred, tuple): action_container_shape = actor_network.spec[ self.tensor_keys.action_pred[:-1] ].shape else: action_container_shape = actor_network.spec.shape target_entropy = -float( actor_network.spec[self.tensor_keys.action_pred] .shape[len(action_container_shape) :] .numel() ) self.register_buffer( "target_entropy", torch.tensor(target_entropy, device=device) ) self.samples_mc_entropy = samples_mc_entropy self._set_in_keys() self.reduction = reduction def _set_in_keys(self): keys = self.actor_network.in_keys keys = set(keys) keys.add(self.tensor_keys.action_target) self._in_keys = sorted(keys, key=str) def _forward_value_estimator_keys(self, **kwargs): pass @property def alpha(self): if self.min_log_alpha is not None: self.log_alpha.data.clamp_(self.min_log_alpha, self.max_log_alpha) with torch.no_grad(): alpha = self.log_alpha.exp() return alpha @property def in_keys(self): if self._in_keys is None: self._set_in_keys() return self._in_keys @in_keys.setter def in_keys(self, values): self._in_keys = values @property def out_keys(self): if self._out_keys is None: keys = [ "loss_log_likelihood", "loss_entropy", "loss_alpha", "alpha", "entropy", ] self._out_keys = keys return self._out_keys @out_keys.setter def out_keys(self, values): self._out_keys = values def get_entropy_bonus(self, dist: d.Distribution) -> torch.Tensor: x = dist.rsample((self.samples_mc_entropy,)) log_p = dist.log_prob(x) # log_p: (batch_size, context_len) return -log_p.mean(axis=0)
[docs] @dispatch def forward(self, tensordict: TensorDictBase) -> TensorDictBase: """Compute the loss for the Online Decision Transformer.""" # extract action targets tensordict = tensordict.copy() target_actions = tensordict.get(self.tensor_keys.action_target) if target_actions.requires_grad: raise RuntimeError("target action cannot be part of a graph.") with self.actor_network_params.to_module(self.actor_network): action_dist = self.actor_network.get_dist(tensordict) log_likelihood = action_dist.log_prob(target_actions) entropy = self.get_entropy_bonus(action_dist) entropy_bonus = self.alpha.detach() * entropy loss_alpha = self.log_alpha.exp() * (entropy - self.target_entropy).detach() out = { "loss_log_likelihood": -log_likelihood, "loss_entropy": -entropy_bonus, "loss_alpha": loss_alpha, "entropy": entropy.detach().mean(), "alpha": self.alpha.detach(), } td_out = TensorDict(out, []) td_out = td_out.named_apply( lambda name, value: _reduce(value, reduction=self.reduction).squeeze(-1) if name.startswith("loss_") else value, batch_size=[], ) return td_out
[docs]class DTLoss(LossModule): r"""TorchRL implementation of the Online Decision Transformer loss. Presented in `"Decision Transformer: Reinforcement Learning via Sequence Modeling" <https://arxiv.org/abs/2106.01345>` Args: actor_network (ProbabilisticActor): stochastic actor Keyword Args: loss_function (str): loss function to use. Defaults to ``"l2"``. reduction (str, optional): Specifies the reduction to apply to the output: ``"none"`` | ``"mean"`` | ``"sum"``. ``"none"``: no reduction will be applied, ``"mean"``: the sum of the output will be divided by the number of elements in the output, ``"sum"``: the output will be summed. Default: ``"mean"``. """ @dataclass class _AcceptedKeys: """Maintains default values for all configurable tensordict keys. This class defines which tensordict keys can be set using '.set_keys(key_name=key_value)' and their default values. Attributes: action_target (NestedKey): The input tensordict key where the action is expected. Defaults to ``"action"``. action_pred (NestedKey): The tensordict key where the output action (from the model) is expected. Defaults to ``"action"``. """ # the "action" contained in the dataset action_target: NestedKey = "action" # the "action" output from the model action_pred: NestedKey = "action" default_keys = _AcceptedKeys() actor_network: TensorDictModule actor_network_params: TensorDictParams target_actor_network_params: TensorDictParams def __init__( self, actor_network: ProbabilisticActor, *, loss_function: str = "l2", reduction: str = None, device: torch.device | None = None, ) -> None: self._in_keys = None self._out_keys = None if reduction is None: reduction = "mean" super().__init__() # Actor Network self.convert_to_functional( actor_network, "actor_network", create_target_params=False, ) self.loss_function = loss_function self.reduction = reduction def _set_in_keys(self): keys = self.actor_network.in_keys keys = set(keys) keys.add(self.tensor_keys.action_pred) keys.add(self.tensor_keys.action_target) self._in_keys = sorted(keys, key=str) def _forward_value_estimator_keys(self, **kwargs) -> None: pass @property def in_keys(self): if self._in_keys is None: self._set_in_keys() return self._in_keys @in_keys.setter def in_keys(self, values): self._in_keys = values @property def out_keys(self): if self._out_keys is None: keys = ["loss"] self._out_keys = keys return self._out_keys @out_keys.setter def out_keys(self, values): self._out_keys = values
[docs] @dispatch def forward(self, tensordict: TensorDictBase) -> TensorDictBase: """Compute the loss for the Online Decision Transformer.""" # extract action targets tensordict = tensordict.copy() target_actions = tensordict.get(self.tensor_keys.action_target).detach() with self.actor_network_params.to_module(self.actor_network): pred_actions = self.actor_network(tensordict).get( self.tensor_keys.action_pred ) loss = distance_loss( pred_actions, target_actions, loss_function=self.loss_function, ) loss = _reduce(loss, reduction=self.reduction) td_out = TensorDict(loss=loss) return td_out

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