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Source code for torchrl.objectives.iql

# 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 warnings
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import torch
from tensordict import TensorDict, TensorDictBase
from tensordict.nn import dispatch, TensorDictModule
from tensordict.utils import NestedKey
from torch import Tensor
from torchrl.data.tensor_specs import TensorSpec
from torchrl.data.utils import _find_action_space

from torchrl.modules import ProbabilisticActor
from torchrl.objectives.common import LossModule
from torchrl.objectives.utils import (
    _GAMMA_LMBDA_DEPREC_ERROR,
    _reduce,
    _vmap_func,
    default_value_kwargs,
    distance_loss,
    ValueEstimators,
)
from torchrl.objectives.value import TD0Estimator, TD1Estimator, TDLambdaEstimator


[docs]class IQLLoss(LossModule): r"""TorchRL implementation of the IQL loss. Presented in "Offline Reinforcement Learning with Implicit Q-Learning" https://arxiv.org/abs/2110.06169 Args: actor_network (ProbabilisticActor): stochastic actor qvalue_network (TensorDictModule): Q(s, a) parametric model value_network (TensorDictModule, optional): V(s) parametric model. Keyword Args: num_qvalue_nets (integer, optional): number of Q-Value networks used. Defaults to ``2``. loss_function (str, optional): loss function to be used with the value function loss. Default is `"smooth_l1"`. temperature (float, optional): Inverse temperature (beta). For smaller hyperparameter values, the objective behaves similarly to behavioral cloning, while for larger values, it attempts to recover the maximum of the Q-function. expectile (float, optional): expectile :math:`\tau`. A larger value of :math:`\tau` is crucial for antmaze tasks that require dynamical programming ("stichting"). priority_key (str, optional): [Deprecated, use .set_keys(priority_key=priority_key) instead] tensordict key where to write the priority (for prioritized replay buffer usage). Default is `"td_error"`. separate_losses (bool, optional): if ``True``, shared parameters between policy and critic will only be trained on the policy loss. Defaults to ``False``, ie. gradients are propagated to shared parameters for both policy and critic losses. 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"``. Examples: >>> import torch >>> from torch import nn >>> from torchrl.data import BoundedTensorSpec >>> from torchrl.modules.distributions.continuous import NormalParamWrapper, TanhNormal >>> from torchrl.modules.tensordict_module.actors import ProbabilisticActor, ValueOperator >>> from torchrl.modules.tensordict_module.common import SafeModule >>> from torchrl.objectives.iql import IQLLoss >>> from tensordict import TensorDict >>> n_act, n_obs = 4, 3 >>> spec = BoundedTensorSpec(-torch.ones(n_act), torch.ones(n_act), (n_act,)) >>> net = NormalParamWrapper(nn.Linear(n_obs, 2 * n_act)) >>> module = SafeModule(net, in_keys=["observation"], out_keys=["loc", "scale"]) >>> actor = ProbabilisticActor( ... module=module, ... in_keys=["loc", "scale"], ... spec=spec, ... distribution_class=TanhNormal) >>> class QValueClass(nn.Module): ... def __init__(self): ... super().__init__() ... self.linear = nn.Linear(n_obs + n_act, 1) ... def forward(self, obs, act): ... return self.linear(torch.cat([obs, act], -1)) >>> qvalue = SafeModule( ... QValueClass(), ... in_keys=["observation", "action"], ... out_keys=["state_action_value"], ... ) >>> value = SafeModule( ... nn.Linear(n_obs, 1), ... in_keys=["observation"], ... out_keys=["state_value"], ... ) >>> loss = IQLLoss(actor, qvalue, value) >>> batch = [2, ] >>> action = spec.rand(batch) >>> data = TensorDict({ ... "observation": torch.randn(*batch, n_obs), ... "action": action, ... ("next", "done"): torch.zeros(*batch, 1, dtype=torch.bool), ... ("next", "terminated"): torch.zeros(*batch, 1, dtype=torch.bool), ... ("next", "reward"): torch.randn(*batch, 1), ... ("next", "observation"): torch.randn(*batch, n_obs), ... }, batch) >>> loss(data) TensorDict( fields={ entropy: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), loss_actor: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), loss_qvalue: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), loss_value: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([]), device=None, is_shared=False) This class is compatible with non-tensordict based modules too and can be used without recurring to any tensordict-related primitive. In this case, the expected keyword arguments are: ``["action", "next_reward", "next_done", "next_terminated"]`` + in_keys of the actor, value, and qvalue network The return value is a tuple of tensors in the following order: ``["loss_actor", "loss_qvalue", "loss_value", "entropy"]``. Examples: >>> import torch >>> from torch import nn >>> from torchrl.data import BoundedTensorSpec >>> from torchrl.modules.distributions.continuous import NormalParamWrapper, TanhNormal >>> from torchrl.modules.tensordict_module.actors import ProbabilisticActor, ValueOperator >>> from torchrl.modules.tensordict_module.common import SafeModule >>> from torchrl.objectives.iql import IQLLoss >>> _ = torch.manual_seed(42) >>> n_act, n_obs = 4, 3 >>> spec = BoundedTensorSpec(-torch.ones(n_act), torch.ones(n_act), (n_act,)) >>> net = NormalParamWrapper(nn.Linear(n_obs, 2 * n_act)) >>> module = SafeModule(net, in_keys=["observation"], out_keys=["loc", "scale"]) >>> actor = ProbabilisticActor( ... module=module, ... in_keys=["loc", "scale"], ... spec=spec, ... distribution_class=TanhNormal) >>> class QValueClass(nn.Module): ... def __init__(self): ... super().__init__() ... self.linear = nn.Linear(n_obs + n_act, 1) ... def forward(self, obs, act): ... return self.linear(torch.cat([obs, act], -1)) >>> qvalue = SafeModule( ... QValueClass(), ... in_keys=["observation", "action"], ... out_keys=["state_action_value"], ... ) >>> value = SafeModule( ... nn.Linear(n_obs, 1), ... in_keys=["observation"], ... out_keys=["state_value"], ... ) >>> loss = IQLLoss(actor, qvalue, value) >>> batch = [2, ] >>> action = spec.rand(batch) >>> loss_actor, loss_qvalue, loss_value, entropy = loss( ... observation=torch.randn(*batch, n_obs), ... action=action, ... next_done=torch.zeros(*batch, 1, dtype=torch.bool), ... next_terminated=torch.zeros(*batch, 1, dtype=torch.bool), ... next_observation=torch.zeros(*batch, n_obs), ... next_reward=torch.randn(*batch, 1)) >>> loss_actor.backward() The output keys can also be filtered using the :meth:`IQLLoss.select_out_keys` method. Examples: >>> _ = loss.select_out_keys('loss_actor', 'loss_qvalue') >>> loss_actor, loss_qvalue = loss( ... observation=torch.randn(*batch, n_obs), ... action=action, ... next_done=torch.zeros(*batch, 1, dtype=torch.bool), ... next_terminated=torch.zeros(*batch, 1, dtype=torch.bool), ... next_observation=torch.zeros(*batch, n_obs), ... next_reward=torch.randn(*batch, 1)) >>> loss_actor.backward() """ @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: value (NestedKey): The input tensordict key where the state value is expected. Will be used for the underlying value estimator. Defaults to ``"state_value"``. action (NestedKey): The input tensordict key where the action is expected. Defaults to ``"action"``. log_prob (NestedKey): The input tensordict key where the log probability is expected. Defaults to ``"_log_prob"``. priority (NestedKey): The input tensordict key where the target priority is written to. Defaults to ``"td_error"``. state_action_value (NestedKey): The input tensordict key where the state action value is expected. Will be used for the underlying value estimator as value key. Defaults to ``"state_action_value"``. reward (NestedKey): The input tensordict key where the reward is expected. Will be used for the underlying value estimator. Defaults to ``"reward"``. done (NestedKey): The key in the input TensorDict that indicates whether a trajectory is done. Will be used for the underlying value estimator. Defaults to ``"done"``. terminated (NestedKey): The key in the input TensorDict that indicates whether a trajectory is terminated. Will be used for the underlying value estimator. Defaults to ``"terminated"``. """ value: NestedKey = "state_value" action: NestedKey = "action" log_prob: NestedKey = "_log_prob" priority: NestedKey = "td_error" state_action_value: NestedKey = "state_action_value" reward: NestedKey = "reward" done: NestedKey = "done" terminated: NestedKey = "terminated" default_keys = _AcceptedKeys() default_value_estimator = ValueEstimators.TD0 out_keys = [ "loss_actor", "loss_qvalue", "loss_value", "entropy", ] def __init__( self, actor_network: ProbabilisticActor, qvalue_network: TensorDictModule, value_network: Optional[TensorDictModule], *, num_qvalue_nets: int = 2, loss_function: str = "smooth_l1", temperature: float = 1.0, expectile: float = 0.5, gamma: float = None, priority_key: str = None, separate_losses: bool = False, reduction: str = None, ) -> None: self._in_keys = None self._out_keys = None if reduction is None: reduction = "mean" super().__init__() self._set_deprecated_ctor_keys(priority=priority_key) # IQL parameter self.temperature = temperature self.expectile = expectile # Actor Network self.convert_to_functional( actor_network, "actor_network", create_target_params=False, ) if separate_losses: # we want to make sure there are no duplicates in the params: the # params of critic must be refs to actor if they're shared policy_params = list(actor_network.parameters()) else: policy_params = None # Value Function Network self.convert_to_functional( value_network, "value_network", create_target_params=False, compare_against=policy_params, ) # Q Function Network self.delay_qvalue = True self.num_qvalue_nets = num_qvalue_nets if separate_losses and policy_params is not None: qvalue_policy_params = list(actor_network.parameters()) + list( value_network.parameters() ) else: qvalue_policy_params = None self.convert_to_functional( qvalue_network, "qvalue_network", num_qvalue_nets, create_target_params=True, compare_against=qvalue_policy_params, ) self.loss_function = loss_function if gamma is not None: raise TypeError(_GAMMA_LMBDA_DEPREC_ERROR) self._vmap_qvalue_networkN0 = _vmap_func( self.qvalue_network, (None, 0), randomness=self.vmap_randomness ) self.reduction = reduction @property def device(self) -> torch.device: raise RuntimeError( "The device attributes of the losses is deprecated since v0.3.", ) def _set_in_keys(self): keys = [ self.tensor_keys.action, ("next", self.tensor_keys.reward), ("next", self.tensor_keys.done), ("next", self.tensor_keys.terminated), *self.actor_network.in_keys, *[("next", key) for key in self.actor_network.in_keys], *self.qvalue_network.in_keys, *self.value_network.in_keys, ] self._in_keys = list(set(keys)) @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
[docs] @staticmethod def loss_value_diff(diff, expectile=0.8): """Loss function for iql expectile value difference.""" weight = torch.where(diff > 0, expectile, (1 - expectile)) return weight * (diff**2)
def _forward_value_estimator_keys(self, **kwargs) -> None: if self._value_estimator is not None: self._value_estimator.set_keys( value=self._tensor_keys.value, reward=self.tensor_keys.reward, done=self.tensor_keys.done, terminated=self.tensor_keys.terminated, ) self._set_in_keys()
[docs] @dispatch def forward(self, tensordict: TensorDictBase) -> TensorDictBase: shape = None if tensordict.ndimension() > 1: shape = tensordict.shape tensordict_reshape = tensordict.reshape(-1) else: tensordict_reshape = tensordict loss_actor, metadata = self.actor_loss(tensordict_reshape) loss_qvalue, metadata_qvalue = self.qvalue_loss(tensordict_reshape) loss_value, metadata_value = self.value_loss(tensordict_reshape) metadata.update(**metadata_qvalue, **metadata_value) if (loss_actor.shape != loss_qvalue.shape) or ( loss_value is not None and loss_actor.shape != loss_value.shape ): raise RuntimeError( f"Losses shape mismatch: {loss_actor.shape}, {loss_qvalue.shape} and {loss_value.shape}" ) tensordict_reshape.set( self.tensor_keys.priority, metadata.pop("td_error").detach().max(0).values ) if shape: tensordict.update(tensordict_reshape.view(shape)) entropy = -tensordict_reshape.get(self.tensor_keys.log_prob).detach() out = { "loss_actor": loss_actor, "loss_qvalue": loss_qvalue, "loss_value": loss_value, "entropy": entropy.mean(), } return TensorDict( out, [], )
def actor_loss(self, tensordict: TensorDictBase) -> Tensor: # KL loss with self.actor_network_params.to_module(self.actor_network): dist = self.actor_network.get_dist(tensordict) log_prob = dist.log_prob(tensordict[self.tensor_keys.action]) # Min Q value td_q = tensordict.select(*self.qvalue_network.in_keys, strict=False) td_q = self._vmap_qvalue_networkN0(td_q, self.target_qvalue_network_params) min_q = td_q.get(self.tensor_keys.state_action_value).min(0)[0].squeeze(-1) if log_prob.shape != min_q.shape: raise RuntimeError( f"Losses shape mismatch: {log_prob.shape} and {min_q.shape}" ) # state value with torch.no_grad(): td_copy = tensordict.select( *self.value_network.in_keys, strict=False ).detach() with self.value_network_params.to_module(self.value_network): self.value_network(td_copy) value = td_copy.get(self.tensor_keys.value).squeeze( -1 ) # assert has no gradient exp_a = torch.exp((min_q - value) * self.temperature) exp_a = exp_a.clamp_max(100) # write log_prob in tensordict for alpha loss tensordict.set(self.tensor_keys.log_prob, log_prob.detach()) loss_actor = -(exp_a * log_prob) loss_actor = _reduce(loss_actor, reduction=self.reduction) return loss_actor, {} def value_loss(self, tensordict: TensorDictBase) -> Tuple[Tensor, Tensor]: # Min Q value td_q = tensordict.select(*self.qvalue_network.in_keys, strict=False) td_q = self._vmap_qvalue_networkN0(td_q, self.target_qvalue_network_params) min_q = td_q.get(self.tensor_keys.state_action_value).min(0)[0].squeeze(-1) # state value td_copy = tensordict.select(*self.value_network.in_keys, strict=False) with self.value_network_params.to_module(self.value_network): self.value_network(td_copy) value = td_copy.get(self.tensor_keys.value).squeeze(-1) value_loss = self.loss_value_diff(min_q - value, self.expectile) value_loss = _reduce(value_loss, reduction=self.reduction) return value_loss, {} def qvalue_loss(self, tensordict: TensorDictBase) -> Tuple[Tensor, Tensor]: obs_keys = self.actor_network.in_keys tensordict = tensordict.select( "next", *obs_keys, self.tensor_keys.action, strict=False ) target_value = self.value_estimator.value_estimate( tensordict, target_params=self.target_value_network_params ).squeeze(-1) tensordict_expand = self._vmap_qvalue_networkN0( tensordict.select(*self.qvalue_network.in_keys, strict=False), self.qvalue_network_params, ) pred_val = tensordict_expand.get(self.tensor_keys.state_action_value).squeeze( -1 ) td_error = (pred_val - target_value).pow(2) loss_qval = distance_loss( pred_val, target_value.expand_as(pred_val), loss_function=self.loss_function, ).sum(0) loss_qval = _reduce(loss_qval, reduction=self.reduction) metadata = {"td_error": td_error.detach()} return loss_qval, metadata
[docs] def make_value_estimator(self, value_type: ValueEstimators = None, **hyperparams): if value_type is None: value_type = self.default_value_estimator self.value_type = value_type value_net = self.value_network hp = dict(default_value_kwargs(value_type)) if hasattr(self, "gamma"): hp["gamma"] = self.gamma hp.update(hyperparams) if value_type is ValueEstimators.TD1: self._value_estimator = TD1Estimator( **hp, value_network=value_net, ) elif value_type is ValueEstimators.TD0: self._value_estimator = TD0Estimator( **hp, value_network=value_net, ) elif value_type is ValueEstimators.GAE: raise NotImplementedError( f"Value type {value_type} it not implemented for loss {type(self)}." ) elif value_type is ValueEstimators.TDLambda: self._value_estimator = TDLambdaEstimator( **hp, value_network=value_net, ) else: raise NotImplementedError(f"Unknown value type {value_type}") tensor_keys = { "value_target": "value_target", "value": self.tensor_keys.value, "reward": self.tensor_keys.reward, "done": self.tensor_keys.done, "terminated": self.tensor_keys.terminated, } self._value_estimator.set_keys(**tensor_keys)
[docs]class DiscreteIQLLoss(IQLLoss): r"""TorchRL implementation of the discrete IQL loss. Presented in "Offline Reinforcement Learning with Implicit Q-Learning" https://arxiv.org/abs/2110.06169 Args: actor_network (ProbabilisticActor): stochastic actor qvalue_network (TensorDictModule): Q(s, a) parametric model. value_network (TensorDictModule, optional): V(s) parametric model. Keyword Args: action_space (str or TensorSpec): Action space. Must be one of ``"one-hot"``, ``"mult_one_hot"``, ``"binary"`` or ``"categorical"``, or an instance of the corresponding specs (:class:`torchrl.data.OneHotDiscreteTensorSpec`, :class:`torchrl.data.MultiOneHotDiscreteTensorSpec`, :class:`torchrl.data.BinaryDiscreteTensorSpec` or :class:`torchrl.data.DiscreteTensorSpec`). num_qvalue_nets (integer, optional): number of Q-Value networks used. Defaults to ``2``. loss_function (str, optional): loss function to be used with the value function loss. Default is `"smooth_l1"`. temperature (float, optional): Inverse temperature (beta). For smaller hyperparameter values, the objective behaves similarly to behavioral cloning, while for larger values, it attempts to recover the maximum of the Q-function. expectile (float, optional): expectile :math:`\tau`. A larger value of :math:`\tau` is crucial for antmaze tasks that require dynamical programming ("stichting"). priority_key (str, optional): [Deprecated, use .set_keys(priority_key=priority_key) instead] tensordict key where to write the priority (for prioritized replay buffer usage). Default is `"td_error"`. separate_losses (bool, optional): if ``True``, shared parameters between policy and critic will only be trained on the policy loss. Defaults to ``False``, ie. gradients are propagated to shared parameters for both policy and critic losses. 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"``. Examples: >>> import torch >>> from torch import nn >>> from torchrl.data.tensor_specs import OneHotDiscreteTensorSpec >>> from torchrl.modules.distributions.discrete import OneHotCategorical >>> from torchrl.modules.tensordict_module.actors import ProbabilisticActor >>> from torchrl.modules.tensordict_module.common import SafeModule >>> from torchrl.objectives.iql import DiscreteIQLLoss >>> from tensordict import TensorDict >>> n_act, n_obs = 4, 3 >>> spec = OneHotDiscreteTensorSpec(n_act) >>> module = SafeModule(nn.Linear(n_obs, n_act), in_keys=["observation"], out_keys=["logits"]) >>> actor = ProbabilisticActor( ... module=module, ... in_keys=["logits"], ... out_keys=["action"], ... spec=spec, ... distribution_class=OneHotCategorical) >>> qvalue = SafeModule( ... nn.Linear(n_obs, n_act), ... in_keys=["observation"], ... out_keys=["state_action_value"], ... ) >>> value = SafeModule( ... nn.Linear(n_obs, 1), ... in_keys=["observation"], ... out_keys=["state_value"], ... ) >>> loss = DiscreteIQLLoss(actor, qvalue, value) >>> batch = [2, ] >>> action = spec.rand(batch).long() >>> data = TensorDict({ ... "observation": torch.randn(*batch, n_obs), ... "action": action, ... ("next", "done"): torch.zeros(*batch, 1, dtype=torch.bool), ... ("next", "terminated"): torch.zeros(*batch, 1, dtype=torch.bool), ... ("next", "reward"): torch.randn(*batch, 1), ... ("next", "observation"): torch.randn(*batch, n_obs), ... }, batch) >>> loss(data) TensorDict( fields={ entropy: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), loss_actor: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), loss_qvalue: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False), loss_value: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)}, batch_size=torch.Size([]), device=None, is_shared=False) This class is compatible with non-tensordict based modules too and can be used without recurring to any tensordict-related primitive. In this case, the expected keyword arguments are: ``["action", "next_reward", "next_done", "next_terminated"]`` + in_keys of the actor, value, and qvalue network The return value is a tuple of tensors in the following order: ``["loss_actor", "loss_qvalue", "loss_value", "entropy"]``. Examples: >>> import torch >>> import torch >>> from torch import nn >>> from torchrl.data.tensor_specs import OneHotDiscreteTensorSpec >>> from torchrl.modules.distributions.discrete import OneHotCategorical >>> from torchrl.modules.tensordict_module.actors import ProbabilisticActor >>> from torchrl.modules.tensordict_module.common import SafeModule >>> from torchrl.objectives.iql import DiscreteIQLLoss >>> _ = torch.manual_seed(42) >>> n_act, n_obs = 4, 3 >>> spec = OneHotDiscreteTensorSpec(n_act) >>> module = SafeModule(nn.Linear(n_obs, n_act), in_keys=["observation"], out_keys=["logits"]) >>> actor = ProbabilisticActor( ... module=module, ... in_keys=["logits"], ... out_keys=["action"], ... spec=spec, ... distribution_class=OneHotCategorical) >>> qvalue = SafeModule( ... nn.Linear(n_obs, n_act), ... in_keys=["observation"], ... out_keys=["state_action_value"], ... ) >>> value = SafeModule( ... nn.Linear(n_obs, 1), ... in_keys=["observation"], ... out_keys=["state_value"], ... ) >>> loss = DiscreteIQLLoss(actor, qvalue, value) >>> batch = [2, ] >>> action = spec.rand(batch).long() >>> loss_actor, loss_qvalue, loss_value, entropy = loss( ... observation=torch.randn(*batch, n_obs), ... action=action, ... next_done=torch.zeros(*batch, 1, dtype=torch.bool), ... next_terminated=torch.zeros(*batch, 1, dtype=torch.bool), ... next_observation=torch.zeros(*batch, n_obs), ... next_reward=torch.randn(*batch, 1)) >>> loss_actor.backward() The output keys can also be filtered using the :meth:`DiscreteIQLLoss.select_out_keys` method. Examples: >>> _ = loss.select_out_keys('loss_actor', 'loss_qvalue', 'loss_value') >>> loss_actor, loss_qvalue, loss_value = loss( ... observation=torch.randn(*batch, n_obs), ... action=action, ... next_done=torch.zeros(*batch, 1, dtype=torch.bool), ... next_terminated=torch.zeros(*batch, 1, dtype=torch.bool), ... next_observation=torch.zeros(*batch, n_obs), ... next_reward=torch.randn(*batch, 1)) >>> loss_actor.backward() """ @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: value (NestedKey): The input tensordict key where the state value is expected. Will be used for the underlying value estimator. Defaults to ``"state_value"``. action (NestedKey): The input tensordict key where the action is expected. Defaults to ``"action"``. log_prob (NestedKey): The input tensordict key where the log probability is expected. Defaults to ``"_log_prob"``. priority (NestedKey): The input tensordict key where the target priority is written to. Defaults to ``"td_error"``. state_action_value (NestedKey): The input tensordict key where the state action value is expected. Will be used for the underlying value estimator as value key. Defaults to ``"state_action_value"``. reward (NestedKey): The input tensordict key where the reward is expected. Will be used for the underlying value estimator. Defaults to ``"reward"``. done (NestedKey): The key in the input TensorDict that indicates whether a trajectory is done. Will be used for the underlying value estimator. Defaults to ``"done"``. terminated (NestedKey): The key in the input TensorDict that indicates whether a trajectory is terminated. Will be used for the underlying value estimator. Defaults to ``"terminated"``. """ value: NestedKey = "state_value" action: NestedKey = "action" log_prob: NestedKey = "_log_prob" priority: NestedKey = "td_error" state_action_value: NestedKey = "state_action_value" reward: NestedKey = "reward" done: NestedKey = "done" terminated: NestedKey = "terminated" default_keys = _AcceptedKeys() default_value_estimator = ValueEstimators.TD0 out_keys = [ "loss_actor", "loss_qvalue", "loss_value", "entropy", ] def __init__( self, actor_network: ProbabilisticActor, qvalue_network: TensorDictModule, value_network: Optional[TensorDictModule], *, action_space: Union[str, TensorSpec] = None, num_qvalue_nets: int = 2, loss_function: str = "smooth_l1", temperature: float = 1.0, expectile: float = 0.5, gamma: float = None, priority_key: str = None, separate_losses: bool = False, reduction: str = None, ) -> None: self._in_keys = None self._out_keys = None if reduction is None: reduction = "mean" if expectile >= 1.0: raise ValueError(f"Expectile should be lower than 1.0 but is {expectile}") super().__init__( actor_network=actor_network, qvalue_network=qvalue_network, value_network=value_network, num_qvalue_nets=num_qvalue_nets, loss_function=loss_function, temperature=temperature, expectile=expectile, gamma=gamma, priority_key=priority_key, separate_losses=separate_losses, ) if action_space is None: warnings.warn( "action_space was not specified. DiscreteIQLLoss will default to 'one-hot'." "This behaviour will be deprecated soon and a space will have to be passed." "Check the DiscreteIQLLoss documentation to see how to pass the action space. " ) action_space = "one-hot" self.action_space = _find_action_space(action_space) self.reduction = reduction def actor_loss(self, tensordict: TensorDictBase) -> Tensor: # KL loss with self.actor_network_params.to_module(self.actor_network): dist = self.actor_network.get_dist(tensordict) log_prob = dist.log_prob(tensordict[self.tensor_keys.action]) # Min Q value td_q = tensordict.select(*self.qvalue_network.in_keys, strict=False) td_q = self._vmap_qvalue_networkN0(td_q, self.target_qvalue_network_params) state_action_value = td_q.get(self.tensor_keys.state_action_value) action = tensordict.get(self.tensor_keys.action) if self.action_space == "categorical": if action.shape != state_action_value.shape: # unsqueeze the action if it lacks on trailing singleton dim action = action.unsqueeze(-1) chosen_state_action_value = torch.gather( state_action_value, -1, index=action ).squeeze(-1) else: action = action.to(torch.float) chosen_state_action_value = (state_action_value * action).sum(-1) min_Q, _ = torch.min(chosen_state_action_value, dim=0) if log_prob.shape != min_Q.shape: raise RuntimeError( f"Losses shape mismatch: {log_prob.shape} and {min_Q.shape}" ) with torch.no_grad(): # state value td_copy = tensordict.select( *self.value_network.in_keys, strict=False ).detach() with self.value_network_params.to_module(self.value_network): self.value_network(td_copy) value = td_copy.get(self.tensor_keys.value).squeeze( -1 ) # assert has no gradient exp_a = torch.exp((min_Q - value) * self.temperature) exp_a = exp_a.clamp_max(100) # write log_prob in tensordict for alpha loss tensordict.set(self.tensor_keys.log_prob, log_prob.detach()) loss_actor = -(exp_a * log_prob) loss_actor = _reduce(loss_actor, reduction=self.reduction) return loss_actor, {} def value_loss(self, tensordict: TensorDictBase) -> Tuple[Tensor, Tensor]: # Min Q value with torch.no_grad(): # Min Q value td_q = tensordict.select(*self.qvalue_network.in_keys, strict=False) td_q = self._vmap_qvalue_networkN0(td_q, self.target_qvalue_network_params) state_action_value = td_q.get(self.tensor_keys.state_action_value) action = tensordict.get(self.tensor_keys.action) if self.action_space == "categorical": if action.shape != state_action_value.shape: # unsqueeze the action if it lacks on trailing singleton dim action = action.unsqueeze(-1) chosen_state_action_value = torch.gather( state_action_value, -1, index=action ).squeeze(-1) else: action = action.to(torch.float) chosen_state_action_value = (state_action_value * action).sum(-1) min_Q, _ = torch.min(chosen_state_action_value, dim=0) # state value td_copy = tensordict.select(*self.value_network.in_keys, strict=False) with self.value_network_params.to_module(self.value_network): self.value_network(td_copy) value = td_copy.get(self.tensor_keys.value).squeeze(-1) value_loss = self.loss_value_diff(min_Q - value, self.expectile) value_loss = _reduce(value_loss, reduction=self.reduction) return value_loss, {} def qvalue_loss(self, tensordict: TensorDictBase) -> Tuple[Tensor, Tensor]: obs_keys = self.actor_network.in_keys next_td = tensordict.select( "next", *obs_keys, self.tensor_keys.action, strict=False ) with torch.no_grad(): target_value = self.value_estimator.value_estimate( next_td, target_params=self.target_value_network_params ).squeeze(-1) # predict current Q value td_q = tensordict.select(*self.qvalue_network.in_keys, strict=False) td_q = self._vmap_qvalue_networkN0(td_q, self.qvalue_network_params) state_action_value = td_q.get(self.tensor_keys.state_action_value) action = tensordict.get(self.tensor_keys.action) if self.action_space == "categorical": if action.shape != state_action_value.shape: # unsqueeze the action if it lacks on trailing singleton dim action = action.unsqueeze(-1) pred_val = torch.gather(state_action_value, -1, index=action).squeeze(-1) else: action = action.to(torch.float) pred_val = (state_action_value * action).sum(-1) td_error = (pred_val - target_value.expand_as(pred_val)).pow(2) loss_qval = distance_loss( pred_val, target_value.expand_as(pred_val), loss_function=self.loss_function, ).sum(0) loss_qval = _reduce(loss_qval, reduction=self.reduction) metadata = {"td_error": td_error.detach()} return loss_qval, metadata

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