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 List, Optional, Tuple, Union
import torch
from tensordict import TensorDict, TensorDictBase, TensorDictParams
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
If a single instance of `qvalue_network` is provided, it will be duplicated ``num_qvalue_nets``
times. If a list of modules is passed, their
parameters will be stacked unless they share the same identity (in which case
the original parameter will be expanded).
.. warning:: When a list of parameters if passed, it will __not__ be compared against the policy parameters
and all the parameters will be considered as untied.
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 (:obj:`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 (:obj:`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``, i.e., 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 Bounded
>>> from torchrl.modules.distributions import NormalParamExtractor, 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 = Bounded(-torch.ones(n_act), torch.ones(n_act), (n_act,))
>>> net = nn.Sequential(nn.Linear(n_obs, 2 * n_act), NormalParamExtractor())
>>> 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 Bounded
>>> from torchrl.modules.distributions import NormalParamExtractor, 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 = Bounded(-torch.ones(n_act), torch.ones(n_act), (n_act,))
>>> net = nn.Sequential(nn.Linear(n_obs, 2 * n_act), NormalParamExtractor())
>>> 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",
]
actor_network: TensorDictModule
actor_network_params: TensorDictParams
target_actor_network_params: TensorDictParams
qvalue_network: TensorDictModule
qvalue_network_params: TensorDictParams
target_qvalue_network_params: TensorDictParams
value_network: TensorDictModule | None
value_network_params: TensorDictParams | None
target_value_network_params: TensorDictParams | None
def __init__(
self,
actor_network: ProbabilisticActor,
qvalue_network: TensorDictModule | List[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._make_vmap()
self.reduction = reduction
def _make_vmap(self):
self._vmap_qvalue_networkN0 = _vmap_func(
self.qvalue_network, (None, 0), randomness=self.vmap_randomness
)
@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:
loss_actor, metadata = self.actor_loss(tensordict)
loss_qvalue, metadata_qvalue = self.qvalue_loss(tensordict)
loss_value, metadata_value = self.value_loss(tensordict)
metadata.update(metadata_qvalue)
metadata.update(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.set(
self.tensor_keys.priority, metadata.pop("td_error").detach().max(0).values
)
entropy = -tensordict.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) -> Tuple[Tensor, dict]:
# 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, dict]:
# 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, dict]:
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.OneHot`,
:class:`torchrl.data.MultiOneHot`,
:class:`torchrl.data.Binary` or :class:`torchrl.data.Categorical`).
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 (:obj:`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 (:obj:`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``, i.e., 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 OneHot
>>> 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 = OneHot(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 OneHot
>>> 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 = OneHot(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",
]
actor_network: TensorDictModule
actor_network_params: TensorDictParams
target_actor_network_params: TensorDictParams
qvalue_network: TensorDictModule
qvalue_network_params: TensorDictParams
target_qvalue_network_params: TensorDictParams
value_network: TensorDictModule | None
value_network_params: TensorDictParams | None
target_value_network_params: TensorDictParams | None
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 behavior 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) -> Tuple[Tensor, dict]:
# 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.ndim < (state_action_value.ndim - (td_q.ndim - tensordict.ndim)):
# unsqueeze the action if it lacks on trailing singleton dim
action = action.unsqueeze(-1)
chosen_state_action_value = torch.vmap(
lambda state_action_value, action: torch.gather(
state_action_value, -1, index=action
).squeeze(-1),
(0, None),
)(state_action_value, action)
elif self.action_space == "one_hot":
action = action.to(torch.float)
chosen_state_action_value = (state_action_value * action).sum(-1)
else:
raise RuntimeError(f"Unknown action space {self.action_space}.")
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, dict]:
# 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.ndim < (
state_action_value.ndim - (td_q.ndim - tensordict.ndim)
):
# unsqueeze the action if it lacks on trailing singleton dim
action = action.unsqueeze(-1)
chosen_state_action_value = torch.vmap(
lambda state_action_value, action: torch.gather(
state_action_value, -1, index=action
).squeeze(-1),
(0, None),
)(state_action_value, action)
elif self.action_space == "one_hot":
action = action.to(torch.float)
chosen_state_action_value = (state_action_value * action).sum(-1)
else:
raise RuntimeError(f"Unknown action space {self.action_space}.")
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, dict]:
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.ndim < (state_action_value.ndim - (td_q.ndim - tensordict.ndim)):
# unsqueeze the action if it lacks on trailing singleton dim
action = action.unsqueeze(-1)
pred_val = torch.vmap(
lambda state_action_value, action: torch.gather(
state_action_value, -1, index=action
).squeeze(-1),
(0, None),
)(state_action_value, action)
elif self.action_space == "one_hot":
action = action.to(torch.float)
pred_val = (state_action_value * action).sum(-1)
else:
raise RuntimeError(f"Unknown action space {self.action_space}.")
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