Source code for torchrl.objectives.crossq
# 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 functools import wraps
from typing import Dict, List, 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 Composite
from torchrl.envs.utils import ExplorationType, set_exploration_type
from torchrl.modules import ProbabilisticActor
from torchrl.objectives.common import LossModule
from torchrl.objectives.utils import (
_cache_values,
_reduce,
_vmap_func,
default_value_kwargs,
distance_loss,
ValueEstimators,
)
from torchrl.objectives.value import TD0Estimator, TD1Estimator, TDLambdaEstimator
def _delezify(func):
@wraps(func)
def new_func(self, *args, **kwargs):
self.target_entropy
return func(self, *args, **kwargs)
return new_func
[docs]class CrossQLoss(LossModule):
"""TorchRL implementation of the CrossQ loss.
Presented in "CROSSQ: BATCH NORMALIZATION IN DEEP REINFORCEMENT LEARNING
FOR GREATER SAMPLE EFFICIENCY AND SIMPLICITY" https://openreview.net/pdf?id=PczQtTsTIX
This class has three loss functions that will be called sequentially by the `forward` method:
:meth:`~.qvalue_loss`, :meth:`~.actor_loss` and :meth:`~.alpha_loss`. Alternatively, they can
be called by the user that order.
Args:
actor_network (ProbabilisticActor): stochastic actor
qvalue_network (TensorDictModule): Q(s, a) parametric model.
This module typically outputs a ``"state_action_value"`` entry.
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.
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"`.
alpha_init (float, optional): initial entropy multiplier.
Default is 1.0.
min_alpha (float, optional): min value of alpha.
Default is None (no minimum value).
max_alpha (float, optional): max value of alpha.
Default is None (no maximum value).
action_spec (TensorSpec, optional): the action tensor spec. If not provided
and the target entropy is ``"auto"``, it will be retrieved from
the actor.
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)`.
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). Defaults to ``"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.crossq import CrossQLoss
>>> 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 ValueClass(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))
>>> module = ValueClass()
>>> qvalue = ValueOperator(
... module=module,
... in_keys=['observation', 'action'])
>>> loss = CrossQLoss(actor, qvalue)
>>> 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={
alpha: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
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_alpha: 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)},
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 and qvalue network.
The return value is a tuple of tensors in the following order:
``["loss_actor", "loss_qvalue", "loss_alpha", "alpha", "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 import CrossQLoss
>>> _ = 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 ValueClass(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))
>>> module = ValueClass()
>>> qvalue = ValueOperator(
... module=module,
... in_keys=['observation', 'action'])
>>> loss = CrossQLoss(actor, qvalue)
>>> batch = [2, ]
>>> action = spec.rand(batch)
>>> 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()
The output keys can also be filtered using the :meth:`CrossQLoss.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:
action (NestedKey): The input tensordict key where the action is expected.
Defaults to ``"advantage"``.
state_action_value (NestedKey): The input tensordict key where the
state action value is expected. Defaults to ``"state_action_value"``.
priority (NestedKey): The input tensordict key where the target priority is written to.
Defaults to ``"td_error"``.
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"``.
log_prob (NestedKey): The input tensordict key where the log probability is expected.
Defaults to ``"_log_prob"``.
"""
action: NestedKey = "action"
state_action_value: NestedKey = "state_action_value"
priority: NestedKey = "td_error"
reward: NestedKey = "reward"
done: NestedKey = "done"
terminated: NestedKey = "terminated"
log_prob: NestedKey = "_log_prob"
default_keys = _AcceptedKeys()
default_value_estimator = ValueEstimators.TD0
actor_network: ProbabilisticActor
actor_network_params: TensorDictParams
qvalue_network: TensorDictModule
qvalue_network_params: TensorDictParams
target_actor_network_params: TensorDictParams
target_qvalue_network_params: TensorDictParams
def __init__(
self,
actor_network: ProbabilisticActor,
qvalue_network: TensorDictModule | List[TensorDictModule],
*,
num_qvalue_nets: int = 2,
loss_function: str = "smooth_l1",
alpha_init: float = 1.0,
min_alpha: float = None,
max_alpha: float = None,
action_spec=None,
fixed_alpha: bool = False,
target_entropy: Union[str, float] = "auto",
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_key=priority_key)
# Actor
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
q_value_policy_params = None
# Q value
self.num_qvalue_nets = num_qvalue_nets
q_value_policy_params = policy_params
self.convert_to_functional(
qvalue_network,
"qvalue_network",
num_qvalue_nets,
create_target_params=False,
compare_against=q_value_policy_params,
)
self.loss_function = loss_function
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)),
)
self._target_entropy = target_entropy
self._action_spec = action_spec
self._make_vmap()
self.reduction = reduction
def _make_vmap(self):
self._vmap_qnetworkN0 = _vmap_func(
self.qvalue_network, (None, 0), randomness=self.vmap_randomness
)
@property
def target_entropy_buffer(self):
"""The target entropy.
This value can be controlled via the `target_entropy` kwarg in the constructor.
"""
return self.target_entropy
@property
def target_entropy(self):
target_entropy = self._buffers.get("_target_entropy", None)
if target_entropy is not None:
return target_entropy
target_entropy = self._target_entropy
action_spec = self._action_spec
actor_network = self.actor_network
device = next(self.parameters()).device
if target_entropy == "auto":
action_spec = (
action_spec
if action_spec is not None
else getattr(actor_network, "spec", None)
)
if action_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 not isinstance(action_spec, Composite):
action_spec = Composite({self.tensor_keys.action: action_spec})
if (
isinstance(self.tensor_keys.action, tuple)
and len(self.tensor_keys.action) > 1
):
action_container_shape = action_spec[self.tensor_keys.action[:-1]].shape
else:
action_container_shape = action_spec.shape
target_entropy = -float(
action_spec[self.tensor_keys.action]
.shape[len(action_container_shape) :]
.numel()
)
delattr(self, "_target_entropy")
self.register_buffer(
"_target_entropy", torch.tensor(target_entropy, device=device)
)
return self._target_entropy
state_dict = _delezify(LossModule.state_dict)
load_state_dict = _delezify(LossModule.load_state_dict)
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] 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 = None
hp = dict(default_value_kwargs(value_type))
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 = {
"reward": self.tensor_keys.reward,
"done": self.tensor_keys.done,
"terminated": self.tensor_keys.terminated,
}
self._value_estimator.set_keys(**tensor_keys)
@property
def device(self) -> torch.device:
for p in self.parameters():
return p.device
raise RuntimeError(
"At least one of the networks of SACLoss must have trainable " "parameters."
)
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._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
@property
def out_keys(self):
if self._out_keys is None:
keys = ["loss_actor", "loss_qvalue", "loss_alpha", "alpha", "entropy"]
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:
"""The forward method.
Computes successively the :meth:`~.qvalue_loss`, :meth:`~.actor_loss` and :meth:`~.alpha_loss`, and returns
a tensordict with these values along with the `"alpha"` value and the `"entropy"` value (detached).
To see what keys are expected in the input tensordict and what keys are expected as output, check the
class's `"in_keys"` and `"out_keys"` attributes.
"""
loss_qvalue, value_metadata = self.qvalue_loss(tensordict)
loss_actor, metadata_actor = self.actor_loss(tensordict)
loss_alpha = self.alpha_loss(log_prob=metadata_actor["log_prob"])
tensordict.set(self.tensor_keys.priority, value_metadata["td_error"])
if loss_actor.shape != loss_qvalue.shape:
raise RuntimeError(
f"Losses shape mismatch: {loss_actor.shape} and {loss_qvalue.shape}"
)
entropy = -metadata_actor["log_prob"]
out = {
"loss_actor": loss_actor,
"loss_qvalue": loss_qvalue,
"loss_alpha": loss_alpha,
"alpha": self._alpha,
"entropy": entropy.detach().mean(),
**metadata_actor,
**value_metadata,
}
td_out = TensorDict(out, [])
# td_out = td_out.named_apply(
# lambda name, value: (
# _reduce(value, reduction=self.reduction)
# if name.startswith("loss_")
# else value
# ),
# batch_size=[],
# )
return td_out
@property
@_cache_values
def _cached_detached_qvalue_params(self):
return self.qvalue_network_params.detach()
[docs] def actor_loss(
self, tensordict: TensorDictBase
) -> Tuple[Tensor, Dict[str, Tensor]]:
"""Compute the actor loss.
The actor loss should be computed after the :meth:`~.qvalue_loss` and before the `~.alpha_loss` which
requires the `log_prob` field of the `metadata` returned by this method.
Args:
tensordict (TensorDictBase): the input data for the loss. Check the class's `in_keys` to see what fields
are required for this to be computed.
Returns: a differentiable tensor with the alpha loss along with a metadata dictionary containing the detached `"log_prob"` of the sampled action.
"""
with set_exploration_type(
ExplorationType.RANDOM
), self.actor_network_params.to_module(self.actor_network):
dist = self.actor_network.get_dist(tensordict)
a_reparm = dist.rsample()
log_prob = dist.log_prob(a_reparm)
td_q = tensordict.select(*self.qvalue_network.in_keys, strict=False)
self.qvalue_network.eval()
td_q.set(self.tensor_keys.action, a_reparm)
td_q = self._vmap_qnetworkN0(
td_q,
self._cached_detached_qvalue_params,
)
min_q = td_q.get(self.tensor_keys.state_action_value).min(0)[0].squeeze(-1)
self.qvalue_network.train()
if log_prob.shape != min_q.shape:
raise RuntimeError(
f"Losses shape mismatch: {log_prob.shape} and {min_q.shape}"
)
actor_loss = self._alpha * log_prob - min_q
return _reduce(actor_loss, reduction=self.reduction), {
"log_prob": log_prob.detach()
}
[docs] def qvalue_loss(
self, tensordict: TensorDictBase
) -> Tuple[Tensor, Dict[str, Tensor]]:
"""Compute the q-value loss.
The q-value loss should be computed before the :meth:`~.actor_loss`.
Args:
tensordict (TensorDictBase): the input data for the loss. Check the class's `in_keys` to see what fields
are required for this to be computed.
Returns: a differentiable tensor with the qvalue loss along with a metadata dictionary containing
the detached `"td_error"` to be used for prioritized sampling.
"""
# # compute next action
with torch.no_grad():
with set_exploration_type(
ExplorationType.RANDOM
), self.actor_network_params.to_module(self.actor_network):
next_tensordict = tensordict.get("next").clone(False)
next_dist = self.actor_network.get_dist(next_tensordict)
next_action = next_dist.sample()
next_tensordict.set(self.tensor_keys.action, next_action)
next_sample_log_prob = next_dist.log_prob(next_action)
combined = torch.cat(
[
tensordict.select(*self.qvalue_network.in_keys, strict=False),
next_tensordict.select(*self.qvalue_network.in_keys, strict=False),
]
)
pred_qs = self._vmap_qnetworkN0(combined, self.qvalue_network_params).get(
self.tensor_keys.state_action_value
)
(current_state_action_value, next_state_action_value) = pred_qs.split(
tensordict.batch_size[0], dim=1
)
# compute target value
if (
next_state_action_value.shape[-len(next_sample_log_prob.shape) :]
!= next_sample_log_prob.shape
):
next_sample_log_prob = next_sample_log_prob.unsqueeze(-1)
next_state_action_value = next_state_action_value.min(0)[0]
next_state_action_value = (
next_state_action_value - self._alpha * next_sample_log_prob
).detach()
target_value = self.value_estimator.value_estimate(
tensordict, next_value=next_state_action_value
).squeeze(-1)
# get current q-values
pred_val = current_state_action_value.squeeze(-1)
# compute loss
td_error = abs(pred_val - target_value)
loss_qval = distance_loss(
pred_val,
target_value.expand_as(pred_val),
loss_function=self.loss_function,
).sum(0)
metadata = {"td_error": td_error.detach().max(0)[0]}
return _reduce(loss_qval, reduction=self.reduction), metadata
[docs] def alpha_loss(self, log_prob: Tensor) -> Tensor:
"""Compute the entropy loss.
The entropy loss should be computed last.
Args:
log_prob (torch.Tensor): a log-probability as computed by the :meth:`~.actor_loss` and returned in the `metadata`.
Returns: a differentiable tensor with the entropy loss.
"""
if self.target_entropy is not None:
# we can compute this loss even if log_alpha is not a parameter
alpha_loss = -self.log_alpha * (log_prob + self.target_entropy)
else:
# placeholder
alpha_loss = torch.zeros_like(log_prob)
return _reduce(alpha_loss, reduction=self.reduction)
@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
