Source code for torchrl.objectives.sac
# 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
import warnings
from dataclasses import dataclass
from functools import wraps
from numbers import Number
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
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 CompositeSpec, TensorSpec
from torchrl.data.utils import _find_action_space
from torchrl.envs.utils import ExplorationType, set_exploration_type
from torchrl.modules import ProbabilisticActor
from torchrl.modules.tensordict_module.actors import ActorCriticWrapper
from torchrl.objectives.common import LossModule
from torchrl.objectives.utils import (
_cache_values,
_GAMMA_LMBDA_DEPREC_ERROR,
_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 SACLoss(LossModule):
"""TorchRL implementation of the SAC loss.
Presented in "Soft Actor-Critic: Off-Policy Maximum Entropy Deep
Reinforcement Learning with a Stochastic Actor" https://arxiv.org/abs/1801.01290
and "Soft Actor-Critic Algorithms and Applications" https://arxiv.org/abs/1812.05905
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.
value_network (TensorDictModule, optional): V(s) parametric model.
This module typically outputs a ``"state_value"`` entry.
.. note::
If not provided, the second version of SAC is assumed, where
only the Q-Value network is needed.
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)`.
delay_actor (bool, optional): Whether to separate the target actor
networks from the actor networks used for data collection.
Default is ``False``.
delay_qvalue (bool, optional): Whether to separate the target Q value
networks from the Q value networks used for data collection.
Default is ``True``.
delay_value (bool, optional): Whether to separate the target value
networks from the value networks used for data collection.
Default is ``True``.
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 BoundedTensorSpec
>>> 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.sac import SACLoss
>>> from tensordict import TensorDict
>>> n_act, n_obs = 4, 3
>>> spec = BoundedTensorSpec(-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'])
>>> module = nn.Linear(n_obs, 1)
>>> value = ValueOperator(
... module=module,
... in_keys=["observation"])
>>> loss = SACLoss(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={
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),
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_alpha", "alpha", "entropy"]`` + ``"loss_value"`` if version one is used.
Examples:
>>> import torch
>>> from torch import nn
>>> from torchrl.data import BoundedTensorSpec
>>> 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.sac import SACLoss
>>> _ = torch.manual_seed(42)
>>> n_act, n_obs = 4, 3
>>> spec = BoundedTensorSpec(-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'])
>>> module = nn.Linear(n_obs, 1)
>>> value = ValueOperator(
... module=module,
... in_keys=["observation"])
>>> loss = SACLoss(actor, qvalue, value)
>>> 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:`SACLoss.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"``.
value (NestedKey): The input tensordict key where the state value is expected.
Will be used for the underlying value estimator. Defaults to ``"state_value"``.
state_action_value (NestedKey): The input tensordict key where the
state action value is expected. Defaults to ``"state_action_value"``.
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"``.
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"``.
"""
action: NestedKey = "action"
value: NestedKey = "state_value"
state_action_value: NestedKey = "state_action_value"
log_prob: NestedKey = "_log_prob"
priority: NestedKey = "td_error"
reward: NestedKey = "reward"
done: NestedKey = "done"
terminated: NestedKey = "terminated"
default_keys = _AcceptedKeys()
default_value_estimator = ValueEstimators.TD0
actor_network: TensorDictModule
qvalue_network: TensorDictModule
value_network: TensorDictModule | None
actor_network_params: TensorDictParams
qvalue_network_params: TensorDictParams
value_network_params: TensorDictParams | None
target_actor_network_params: TensorDictParams
target_qvalue_network_params: TensorDictParams
target_value_network_params: TensorDictParams | None
def __init__(
self,
actor_network: ProbabilisticActor,
qvalue_network: TensorDictModule | List[TensorDictModule],
value_network: Optional[TensorDictModule] = None,
*,
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",
delay_actor: bool = False,
delay_qvalue: bool = True,
delay_value: bool = True,
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_key=priority_key)
# Actor
self.delay_actor = delay_actor
self.convert_to_functional(
actor_network,
"actor_network",
create_target_params=self.delay_actor,
)
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
# Value
if value_network is not None:
self._version = 1
self.delay_value = delay_value
self.convert_to_functional(
value_network,
"value_network",
create_target_params=self.delay_value,
compare_against=policy_params,
)
else:
self._version = 2
# Q value
self.delay_qvalue = delay_qvalue
self.num_qvalue_nets = num_qvalue_nets
if self._version == 1:
if separate_losses:
value_params = list(value_network.parameters())
q_value_policy_params = policy_params + value_params
else:
q_value_policy_params = policy_params
else:
q_value_policy_params = policy_params
self.convert_to_functional(
qvalue_network,
"qvalue_network",
num_qvalue_nets,
create_target_params=self.delay_qvalue,
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
if self._version == 1:
self.__dict__["actor_critic"] = ActorCriticWrapper(
self.actor_network, self.value_network
)
if gamma is not None:
raise TypeError(_GAMMA_LMBDA_DEPREC_ERROR)
self._make_vmap()
self.reduction = reduction
def _make_vmap(self):
self._vmap_qnetworkN0 = _vmap_func(
self.qvalue_network, (None, 0), randomness=self.vmap_randomness
)
if self._version == 1:
self._vmap_qnetwork00 = _vmap_func(
self.qvalue_network, randomness=self.vmap_randomness
)
@property
def target_entropy_buffer(self):
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, CompositeSpec):
action_spec = CompositeSpec({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
if self._version == 1:
value_net = self.actor_critic
elif self._version == 2:
# we will take care of computing the next value inside this module
value_net = None
else:
# unreachable
raise NotImplementedError
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 = {
"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)
@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,
]
if self._version == 1:
keys.extend(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
@property
def out_keys(self):
if self._out_keys is None:
keys = ["loss_actor", "loss_qvalue", "loss_alpha", "alpha", "entropy"]
if self._version == 1:
keys.append("loss_value")
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:
shape = None
if tensordict.ndimension() > 1:
shape = tensordict.shape
tensordict_reshape = tensordict.reshape(-1)
else:
tensordict_reshape = tensordict
if self._version == 1:
loss_qvalue, value_metadata = self._qvalue_v1_loss(tensordict_reshape)
loss_value, _ = self._value_loss(tensordict_reshape)
else:
loss_qvalue, value_metadata = self._qvalue_v2_loss(tensordict_reshape)
loss_value = None
loss_actor, metadata_actor = self._actor_loss(tensordict_reshape)
loss_alpha = self._alpha_loss(log_prob=metadata_actor["log_prob"])
tensordict_reshape.set(self.tensor_keys.priority, value_metadata["td_error"])
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}"
)
if shape:
tensordict.update(tensordict_reshape.view(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(),
}
if self._version == 1:
out["loss_value"] = loss_value
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()
def _actor_loss(
self, tensordict: TensorDictBase
) -> Tuple[Tensor, Dict[str, Tensor]]:
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)
td_q.set(self.tensor_keys.action, a_reparm)
td_q = self._vmap_qnetworkN0(
td_q,
self._cached_detached_qvalue_params, # should we clone?
)
min_q_logprob = (
td_q.get(self.tensor_keys.state_action_value).min(0)[0].squeeze(-1)
)
if log_prob.shape != min_q_logprob.shape:
raise RuntimeError(
f"Losses shape mismatch: {log_prob.shape} and {min_q_logprob.shape}"
)
return self._alpha * log_prob - min_q_logprob, {"log_prob": log_prob.detach()}
@property
@_cache_values
def _cached_target_params_actor_value(self):
return TensorDict._new_unsafe(
{
"module": {
"0": self.target_actor_network_params,
"1": self.target_value_network_params,
}
},
torch.Size([]),
)
def _qvalue_v1_loss(
self, tensordict: TensorDictBase
) -> Tuple[Tensor, Dict[str, Tensor]]:
target_params = self._cached_target_params_actor_value
with set_exploration_type(self.deterministic_sampling_mode):
target_value = self.value_estimator.value_estimate(
tensordict, target_params=target_params
).squeeze(-1)
# Q-nets must be trained independently: as such, we split the data in 2
# if required and train each q-net on one half of the data.
shape = tensordict.shape
if shape[0] % self.num_qvalue_nets != 0:
raise RuntimeError(
f"Batch size={tensordict.shape} is incompatible "
f"with num_qvqlue_nets={self.num_qvalue_nets}."
)
tensordict_chunks = tensordict.reshape(
self.num_qvalue_nets, -1, *tensordict.shape[1:]
)
target_chunks = target_value.reshape(
self.num_qvalue_nets, -1, *target_value.shape[1:]
)
# if vmap=True, it is assumed that the input tensordict must be cast to the param shape
tensordict_chunks = self._vmap_qnetwork00(
tensordict_chunks, self.qvalue_network_params
)
pred_val = tensordict_chunks.get(self.tensor_keys.state_action_value)
pred_val = pred_val.squeeze(-1)
loss_value = distance_loss(
pred_val, target_chunks, loss_function=self.loss_function
).view(*shape)
metadata = {"td_error": (pred_val - target_chunks).pow(2).flatten(0, 1)}
return loss_value, metadata
def _compute_target_v2(self, tensordict) -> Tensor:
r"""Value network for SAC v2.
SAC v2 is based on a value estimate of the form:
.. math::
V = Q(s,a) - \alpha * \log p(a | s)
This class computes this value given the actor and qvalue network
"""
tensordict = tensordict.clone(False)
# get actions and log-probs
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.rsample()
next_tensordict.set(self.tensor_keys.action, next_action)
next_sample_log_prob = next_dist.log_prob(next_action)
# get q-values
next_tensordict_expand = self._vmap_qnetworkN0(
next_tensordict, self.target_qvalue_network_params
)
state_action_value = next_tensordict_expand.get(
self.tensor_keys.state_action_value
)
if (
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_value = state_action_value - self._alpha * next_sample_log_prob
next_state_value = next_state_value.min(0)[0]
tensordict.set(
("next", self.value_estimator.tensor_keys.value), next_state_value
)
target_value = self.value_estimator.value_estimate(tensordict).squeeze(-1)
return target_value
def _qvalue_v2_loss(
self, tensordict: TensorDictBase
) -> Tuple[Tensor, Dict[str, Tensor]]:
# we pass the alpha value to the tensordict. Since it's a scalar, we must erase the batch-size first.
target_value = self._compute_target_v2(tensordict)
tensordict_expand = self._vmap_qnetworkN0(
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 = 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 loss_qval, metadata
def _value_loss(
self, tensordict: TensorDictBase
) -> Tuple[Tensor, Dict[str, Tensor]]:
# value loss
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)
pred_val = td_copy.get(self.tensor_keys.value).squeeze(-1)
with self.target_actor_network_params.to_module(self.actor_network):
action_dist = self.actor_network.get_dist(td_copy) # resample an action
action = action_dist.rsample()
td_copy.set(self.tensor_keys.action, action, inplace=False)
td_copy = self._vmap_qnetworkN0(
td_copy,
self.target_qvalue_network_params,
)
min_qval = (
td_copy.get(self.tensor_keys.state_action_value).squeeze(-1).min(0)[0]
)
log_p = action_dist.log_prob(action)
if log_p.shape != min_qval.shape:
raise RuntimeError(
f"Losses shape mismatch: {min_qval.shape} and {log_p.shape}"
)
target_val = min_qval - self._alpha * log_p
loss_value = distance_loss(
pred_val, target_val, loss_function=self.loss_function
)
return loss_value, {}
def _alpha_loss(self, log_prob: Tensor) -> Tensor:
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 alpha_loss
@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
[docs]class DiscreteSACLoss(LossModule):
"""Discrete SAC Loss module.
Args:
actor_network (ProbabilisticActor): the actor to be trained
qvalue_network (TensorDictModule): a single Q-value network that will be multiplicated as many times as needed.
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_actions (int, optional): number of actions in the action space.
To be provided if target_entropy is set to "auto".
num_qvalue_nets (int, optional): Number of Q-value networks to be trained. Default is 2.
loss_function (str, optional): loss function to be used for the Q-value. Can be one of `"smooth_l1"`, "l2",
"l1", 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).
fixed_alpha (bool, optional): whether alpha should be trained to match a target entropy. Default is ``False``.
target_entropy_weight (float, optional): weight for the target entropy term.
target_entropy (Union[str, Number], optional): Target entropy for the stochastic policy. Default is "auto".
delay_qvalue (bool, optional): Whether to separate the target Q value networks from the Q value networks used
for data collection. Default is ``False``.
priority_key (str, optional): [Deprecated, use .set_keys(priority_key=priority_key) instead]
Key where to write the priority value for prioritized replay buffers.
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 OneHotDiscreteTensorSpec
>>> from torchrl.modules.distributions import NormalParamExtractor, OneHotCategorical
>>> from torchrl.modules.tensordict_module.actors import ProbabilisticActor, ValueOperator
>>> from torchrl.modules.tensordict_module.common import SafeModule
>>> from torchrl.objectives.sac import DiscreteSACLoss
>>> from tensordict import TensorDict
>>> from tensordict.nn import TensorDictModule
>>> n_act, n_obs = 4, 3
>>> spec = OneHotDiscreteTensorSpec(n_act)
>>> module = TensorDictModule(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 = TensorDictModule(
... nn.Linear(n_obs, n_act),
... in_keys=["observation"],
... out_keys=["action_value"],
... )
>>> loss = DiscreteSACLoss(actor, qvalue, action_space=spec, num_actions=spec.space.n)
>>> 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"]``
The output keys can also be filtered using :meth:`DiscreteSACLoss.select_out_keys` method.
Examples:
>>> import torch
>>> from torch import nn
>>> from torchrl.data.tensor_specs import OneHotDiscreteTensorSpec
>>> from torchrl.modules.distributions import NormalParamExtractor, OneHotCategorical
>>> from torchrl.modules.tensordict_module.actors import ProbabilisticActor, ValueOperator
>>> from torchrl.modules.tensordict_module.common import SafeModule
>>> from torchrl.objectives.sac import DiscreteSACLoss
>>> n_act, n_obs = 4, 3
>>> spec = OneHotDiscreteTensorSpec(n_act)
>>> net = nn.Sequential(nn.Linear(n_obs, 2 * n_act), NormalParamExtractor())
>>> module = SafeModule(net, in_keys=["observation"], out_keys=["logits"])
>>> actor = ProbabilisticActor(
... module=module,
... in_keys=["logits"],
... out_keys=["action"],
... spec=spec,
... distribution_class=OneHotCategorical)
>>> class ValueClass(nn.Module):
... def __init__(self):
... super().__init__()
... self.linear = nn.Linear(n_obs, n_act)
... def forward(self, obs):
... return self.linear(obs)
>>> module = ValueClass()
>>> qvalue = ValueOperator(
... module=module,
... in_keys=['observation'])
>>> loss = DiscreteSACLoss(actor, qvalue, num_actions=actor.spec["action"].space.n)
>>> batch = [2, ]
>>> action = spec.rand(batch)
>>> # filter output keys to "loss_actor", and "loss_qvalue"
>>> _ = 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 ``"action"``.
value (NestedKey): The input tensordict key where the state value is expected.
Will be used for the underlying value estimator. Defaults to ``"state_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"``.
"""
action: NestedKey = "action"
value: NestedKey = "state_value"
action_value: NestedKey = "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
delay_actor: bool = False
out_keys = [
"loss_actor",
"loss_qvalue",
"loss_alpha",
"alpha",
"entropy",
]
actor_network: TensorDictModule
qvalue_network: TensorDictModule
value_network: TensorDictModule | None
actor_network_params: TensorDictParams
qvalue_network_params: TensorDictParams
value_network_params: TensorDictParams | None
target_actor_network_params: TensorDictParams
target_qvalue_network_params: TensorDictParams
target_value_network_params: TensorDictParams | None
def __init__(
self,
actor_network: ProbabilisticActor,
qvalue_network: TensorDictModule,
*,
action_space: Union[str, TensorSpec] = None,
num_actions: Optional[int] = None,
num_qvalue_nets: int = 2,
loss_function: str = "smooth_l1",
alpha_init: float = 1.0,
min_alpha: float = None,
max_alpha: float = None,
fixed_alpha: bool = False,
target_entropy_weight: float = 0.98,
target_entropy: Union[str, Number] = "auto",
delay_qvalue: bool = True,
priority_key: str = None,
separate_losses: bool = False,
reduction: str = None,
):
if reduction is None:
reduction = "mean"
self._in_keys = None
super().__init__()
self._set_deprecated_ctor_keys(priority_key=priority_key)
self.convert_to_functional(
actor_network,
"actor_network",
create_target_params=self.delay_actor,
)
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
self.delay_qvalue = delay_qvalue
self.convert_to_functional(
qvalue_network,
"qvalue_network",
num_qvalue_nets,
create_target_params=self.delay_qvalue,
compare_against=policy_params,
)
self.num_qvalue_nets = num_qvalue_nets
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)),
)
if action_space is None:
warnings.warn(
"action_space was not specified. DiscreteSACLoss will default to 'one-hot'."
"This behaviour will be deprecated soon and a space will have to be passed. "
"Check the DiscreteSACLoss documentation to see how to pass the action space. "
)
action_space = "one-hot"
self.action_space = _find_action_space(action_space)
if target_entropy == "auto":
if num_actions is None:
raise ValueError(
"num_actions needs to be provided if target_entropy == 'auto'"
)
target_entropy = -float(np.log(1.0 / num_actions) * target_entropy_weight)
self.register_buffer(
"target_entropy", torch.tensor(target_entropy, device=device)
)
self._make_vmap()
self.reduction = reduction
def _make_vmap(self):
self._vmap_qnetworkN0 = _vmap_func(
self.qvalue_network, (None, 0), randomness=self.vmap_randomness
)
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()
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
[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_value, metadata_value = self._value_loss(tensordict_reshape)
loss_actor, metadata_actor = self._actor_loss(tensordict_reshape)
loss_alpha = self._alpha_loss(
log_prob=metadata_actor["log_prob"],
)
tensordict_reshape.set(self.tensor_keys.priority, metadata_value["td_error"])
if loss_actor.shape != loss_value.shape:
raise RuntimeError(
f"Losses shape mismatch: {loss_actor.shape}, and {loss_value.shape}"
)
if shape:
tensordict.update(tensordict_reshape.view(shape))
entropy = -metadata_actor["log_prob"]
out = {
"loss_actor": loss_actor,
"loss_qvalue": loss_value,
"loss_alpha": loss_alpha,
"alpha": self._alpha,
"entropy": entropy.detach().mean(),
}
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
def _compute_target(self, tensordict) -> Tensor:
r"""Value network for SAC v2.
SAC v2 is based on a value estimate of the form:
.. math::
V = Q(s,a) - \alpha * \log p(a | s)
This class computes this value given the actor and qvalue network
"""
tensordict = tensordict.clone(False)
# get actions and log-probs
with torch.no_grad():
next_tensordict = tensordict.get("next").clone(False)
# get probs and log probs for actions computed from "next"
with self.actor_network_params.to_module(self.actor_network):
next_dist = self.actor_network.get_dist(next_tensordict)
next_prob = next_dist.probs
next_log_prob = torch.log(torch.where(next_prob == 0, 1e-8, next_prob))
# get q-values for all actions
next_tensordict_expand = self._vmap_qnetworkN0(
next_tensordict, self.target_qvalue_network_params
)
next_action_value = next_tensordict_expand.get(
self.tensor_keys.action_value
)
# like in continuous SAC, we take the minimum of the value ensemble and subtract the entropy term
next_state_value = next_action_value.min(0)[0] - self._alpha * next_log_prob
# unlike in continuous SAC, we can compute the exact expectation over all discrete actions
next_state_value = (next_prob * next_state_value).sum(-1).unsqueeze(-1)
tensordict.set(
("next", self.value_estimator.tensor_keys.value), next_state_value
)
target_value = self.value_estimator.value_estimate(tensordict).squeeze(-1)
return target_value
def _value_loss(
self, tensordict: TensorDictBase
) -> Tuple[Tensor, Dict[str, Tensor]]:
target_value = self._compute_target(tensordict)
tensordict_expand = self._vmap_qnetworkN0(
tensordict.select(*self.qvalue_network.in_keys, strict=False),
self.qvalue_network_params,
)
action_value = tensordict_expand.get(self.tensor_keys.action_value)
action = tensordict.get(self.tensor_keys.action)
action = action.expand((action_value.shape[0], *action.shape)) # Add vmap dim
# TODO this block comes from the dqn loss, we need to swap all these with a proper
# helper function which selects the value given the action for all discrete spaces
if self.action_space == "categorical":
if action.shape != action_value.shape:
# unsqueeze the action if it lacks on trailing singleton dim
action = action.unsqueeze(-1)
chosen_action_value = torch.gather(action_value, -1, index=action).squeeze(
-1
)
else:
action = action.to(torch.float)
chosen_action_value = (action_value * action).sum(-1)
td_error = torch.abs(chosen_action_value - target_value)
loss_qval = distance_loss(
chosen_action_value,
target_value.expand_as(chosen_action_value),
loss_function=self.loss_function,
).sum(0)
metadata = {
"td_error": td_error.detach().max(0)[0],
}
return loss_qval, metadata
def _actor_loss(
self, tensordict: TensorDictBase
) -> Tuple[Tensor, Dict[str, Tensor]]:
# get probs and log probs for actions
with self.actor_network_params.to_module(self.actor_network):
dist = self.actor_network.get_dist(tensordict.clone(False))
prob = dist.probs
log_prob = dist.logits
td_q = tensordict.select(*self.qvalue_network.in_keys, strict=False)
td_q = self._vmap_qnetworkN0(
td_q, self._cached_detached_qvalue_params # should we clone?
)
min_q = td_q.get(self.tensor_keys.action_value).min(0)[0]
if log_prob.shape != min_q.shape:
raise RuntimeError(
f"Losses shape mismatch: {log_prob.shape} and {min_q.shape}"
)
# like in continuous SAC, we take the entropy term and subtract the minimum of the value ensemble
loss = self._alpha * log_prob - min_q
# unlike in continuous SAC, we can compute the exact expectation over all discrete actions
loss = (prob * loss).sum(-1)
return loss, {"log_prob": (log_prob * prob).sum(-1).detach()}
def _alpha_loss(self, log_prob: Tensor) -> Tensor:
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 alpha_loss
@property
def _alpha(self):
if self.min_log_alpha is not None:
self.log_alpha.data = 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
@_cache_values
def _cached_detached_qvalue_params(self):
return self.qvalue_network_params.detach()
[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
hp = dict(default_value_kwargs(value_type))
hp.update(hyperparams)
if hasattr(self, "gamma"):
hp["gamma"] = self.gamma
if value_type is ValueEstimators.TD1:
self._value_estimator = TD1Estimator(
**hp,
value_network=None,
)
elif value_type is ValueEstimators.TD0:
self._value_estimator = TD0Estimator(
**hp,
value_network=None,
)
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=None,
)
else:
raise NotImplementedError(f"Unknown value type {value_type}")
tensor_keys = {
"value": self.tensor_keys.value,
"value_target": "value_target",
"reward": self.tensor_keys.reward,
"done": self.tensor_keys.done,
"terminated": self.tensor_keys.terminated,
}
self._value_estimator.set_keys(**tensor_keys)
