Source code for torchrl.objectives.td3_bc
# 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
from dataclasses import dataclass
from typing import List, Optional, Tuple
import torch
from tensordict import TensorDict, TensorDictBase, TensorDictParams
from tensordict.nn import dispatch, TensorDictModule
from tensordict.utils import NestedKey
from torchrl.data.tensor_specs import Bounded, Composite, TensorSpec
from torchrl.envs.utils import step_mdp
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
[docs]class TD3BCLoss(LossModule):
r"""TD3+BC Loss Module.
Implementation of the TD3+BC loss presented in the paper `"A Minimalist Approach to
Offline Reinforcement Learning" <https://arxiv.org/pdf/2106.06860>`.
This class incorporates two loss functions, executed sequentially within the `forward` method:
1. :meth:`~.qvalue_loss`
2. :meth:`~.actor_loss`
Users also have the option to call these functions directly in the same order if preferred.
Args:
actor_network (TensorDictModule): the actor to be trained
qvalue_network (TensorDictModule): a single Q-value network or a list of
Q-value networks.
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:
bounds (tuple of float, optional): the bounds of the action space.
Exclusive with ``action_spec``. Either this or ``action_spec`` must
be provided.
action_spec (TensorSpec, optional): the action spec.
Exclusive with ``bounds``. Either this or ``bounds`` must be provided.
num_qvalue_nets (int, optional): Number of Q-value networks to be
trained. Default is ``2``.
policy_noise (:obj:`float`, optional): Standard deviation for the target
policy action noise. Default is ``0.2``.
noise_clip (:obj:`float`, optional): Clipping range value for the sampled
target policy action noise. Default is ``0.5``.
alpha (:obj:`float`, optional): Weight for the behavioral cloning loss.
Defaults to ``2.5``.
priority_key (str, optional): Key where to write the priority value
for prioritized replay buffers. Default is
`"td_error"`.
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"``.
delay_actor (bool, optional): whether to separate the target actor
networks from the actor networks used for
data collection. Default is ``True``.
delay_qvalue (bool, optional): Whether to separate the target Q value
networks from the Q value networks used
for data collection. Default is ``True``.
spec (TensorSpec, optional): the action tensor spec. If not provided
and the target entropy is ``"auto"``, it will be retrieved from
the actor.
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 Actor, ProbabilisticActor, ValueOperator
>>> from torchrl.modules.tensordict_module.common import SafeModule
>>> from torchrl.objectives.td3_bc import TD3BCLoss
>>> from tensordict import TensorDict
>>> n_act, n_obs = 4, 3
>>> spec = Bounded(-torch.ones(n_act), torch.ones(n_act), (n_act,))
>>> module = nn.Linear(n_obs, n_act)
>>> actor = Actor(
... module=module,
... spec=spec)
>>> 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 = TD3BCLoss(actor, qvalue, action_spec=actor.spec)
>>> 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={
bc_loss: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
lmbd: 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),
next_state_value: Tensor(shape=torch.Size([2]), device=cpu, dtype=torch.float32, is_shared=False),
pred_value: Tensor(shape=torch.Size([2, 2]), device=cpu, dtype=torch.float32, is_shared=False),
state_action_value_actor: Tensor(shape=torch.Size([2, 2]), device=cpu, dtype=torch.float32, is_shared=False),
target_value: Tensor(shape=torch.Size([2]), 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", "bc_loss, "lmbd", "pred_value", "state_action_value_actor", "next_state_value", "target_value",]``.
Examples:
>>> import torch
>>> from torch import nn
>>> from torchrl.data import Bounded
>>> from torchrl.modules.tensordict_module.actors import Actor, ValueOperator
>>> from torchrl.objectives.td3_bc import TD3BCLoss
>>> n_act, n_obs = 4, 3
>>> spec = Bounded(-torch.ones(n_act), torch.ones(n_act), (n_act,))
>>> module = nn.Linear(n_obs, n_act)
>>> actor = Actor(
... module=module,
... spec=spec)
>>> 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 = TD3BCLoss(actor, qvalue, action_spec=actor.spec)
>>> _ = loss.select_out_keys("loss_actor", "loss_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_reward=torch.randn(*batch, 1),
... next_observation=torch.randn(*batch, n_obs))
>>> 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"``.
state_action_value (NestedKey): The input tensordict key where the state action value is expected.
Will be used for the underlying value estimator. 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"``.
"""
action: NestedKey = "action"
state_action_value: NestedKey = "state_action_value"
priority: NestedKey = "td_error"
reward: NestedKey = "reward"
done: NestedKey = "done"
terminated: NestedKey = "terminated"
default_keys = _AcceptedKeys()
default_value_estimator = ValueEstimators.TD0
out_keys = [
"loss_actor",
"loss_qvalue",
"bc_loss",
"lmbd",
"pred_value",
"state_action_value_actor",
"next_state_value",
"target_value",
]
actor_network: TensorDictModule
qvalue_network: TensorDictModule
actor_network_params: TensorDictParams
qvalue_network_params: TensorDictParams
target_actor_network_params: TensorDictParams
target_qvalue_network_params: TensorDictParams
def __init__(
self,
actor_network: TensorDictModule,
qvalue_network: TensorDictModule | List[TensorDictModule],
*,
action_spec: TensorSpec = None,
bounds: Optional[Tuple[float]] = None,
num_qvalue_nets: int = 2,
policy_noise: float = 0.2,
noise_clip: float = 0.5,
alpha: float = 2.5,
loss_function: str = "smooth_l1",
delay_actor: bool = True,
delay_qvalue: bool = True,
priority_key: str = None,
separate_losses: bool = False,
reduction: str = None,
) -> None:
if reduction is None:
reduction = "mean"
super().__init__()
self._in_keys = None
self._set_deprecated_ctor_keys(priority=priority_key)
self.delay_actor = delay_actor
self.delay_qvalue = delay_qvalue
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.convert_to_functional(
qvalue_network,
"qvalue_network",
num_qvalue_nets,
create_target_params=self.delay_qvalue,
compare_against=policy_params,
)
for p in self.parameters():
device = p.device
break
else:
device = None
self.num_qvalue_nets = num_qvalue_nets
self.loss_function = loss_function
self.policy_noise = policy_noise
self.noise_clip = noise_clip
self.alpha = alpha
if not ((action_spec is not None) ^ (bounds is not None)):
raise ValueError(
"One of 'bounds' and 'action_spec' must be provided, "
f"but not both or none. Got bounds={bounds} and action_spec={action_spec}."
)
elif action_spec is not None:
if isinstance(action_spec, Composite):
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
action_spec = action_spec[self.tensor_keys.action][
(0,) * len(action_container_shape)
]
if not isinstance(action_spec, Bounded):
raise ValueError(
f"action_spec is not of type Bounded but {type(action_spec)}."
)
low = action_spec.space.low
high = action_spec.space.high
else:
low, high = bounds
if not isinstance(low, torch.Tensor):
low = torch.tensor(low)
if not isinstance(high, torch.Tensor):
high = torch.tensor(high, device=low.device, dtype=low.dtype)
if (low > high).any():
raise ValueError("Got a low bound higher than a high bound.")
if device is not None:
low = low.to(device)
high = high.to(device)
self.register_buffer("max_action", high)
self.register_buffer("min_action", low)
self._make_vmap()
self.reduction = reduction
def _make_vmap(self):
self._vmap_qvalue_network00 = _vmap_func(
self.qvalue_network, randomness=self.vmap_randomness
)
self._vmap_actor_network00 = _vmap_func(
self.actor_network, 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.state_action_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
@property
@_cache_values
def _cached_detach_qvalue_network_params(self):
return self.qvalue_network_params.detach()
@property
@_cache_values
def _cached_stack_actor_params(self):
return torch.stack(
[self.actor_network_params, self.target_actor_network_params], 0
)
[docs] def actor_loss(self, tensordict) -> Tuple[torch.Tensor, dict]:
"""Compute the actor loss.
The actor loss should be computed after the :meth:`~.qvalue_loss` and is usually delayed 1-3 critic updates.
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 actor loss along with a metadata dictionary containing the detached `"bc_loss"`
used in the combined actor loss as well as the detached `"state_action_value_actor"` used to calculate the lambda
value, and the lambda value `"lmbd"` itself.
"""
tensordict_actor_grad = tensordict.select(
*self.actor_network.in_keys, strict=False
)
with self.actor_network_params.to_module(self.actor_network):
tensordict_actor_grad = self.actor_network(tensordict_actor_grad)
actor_loss_td = tensordict_actor_grad.select(
*self.qvalue_network.in_keys, strict=False
).expand(
self.num_qvalue_nets, *tensordict_actor_grad.batch_size
) # for actor loss
state_action_value_actor = (
self._vmap_qvalue_network00(
actor_loss_td,
self._cached_detach_qvalue_network_params,
)
.get(self.tensor_keys.state_action_value)
.squeeze(-1)
)
bc_loss = torch.nn.functional.mse_loss(
tensordict_actor_grad.get(self.tensor_keys.action),
tensordict.get(self.tensor_keys.action),
)
lmbd = self.alpha / state_action_value_actor[0].abs().mean().detach()
loss_actor = -lmbd * state_action_value_actor[0] + bc_loss
metadata = {
"state_action_value_actor": state_action_value_actor[0].detach(),
"bc_loss": bc_loss.detach(),
"lmbd": lmbd,
}
loss_actor = _reduce(loss_actor, reduction=self.reduction)
return loss_actor, metadata
[docs] def qvalue_loss(self, tensordict) -> Tuple[torch.Tensor, dict]:
"""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, the detached `"next_state_value"`, the detached `"pred_value"`, and the detached `"target_value"`.
"""
tensordict = tensordict.clone(False)
act = tensordict.get(self.tensor_keys.action)
# computing early for reprod
noise = (torch.randn_like(act) * self.policy_noise).clamp(
-self.noise_clip, self.noise_clip
)
with torch.no_grad():
next_td_actor = step_mdp(tensordict).select(
*self.actor_network.in_keys, strict=False
) # next_observation ->
with self.target_actor_network_params.to_module(self.actor_network):
next_td_actor = self.actor_network(next_td_actor)
next_action = (next_td_actor.get(self.tensor_keys.action) + noise).clamp(
self.min_action, self.max_action
)
next_td_actor.set(
self.tensor_keys.action,
next_action,
)
next_val_td = next_td_actor.select(
*self.qvalue_network.in_keys, strict=False
).expand(
self.num_qvalue_nets, *next_td_actor.batch_size
) # for next value estimation
next_target_q1q2 = (
self._vmap_qvalue_network00(
next_val_td,
self.target_qvalue_network_params,
)
.get(self.tensor_keys.state_action_value)
.squeeze(-1)
)
# min over the next target qvalues
next_target_qvalue = next_target_q1q2.min(0)[0]
# set next target qvalues
tensordict.set(
("next", self.tensor_keys.state_action_value),
next_target_qvalue.unsqueeze(-1),
)
qval_td = tensordict.select(*self.qvalue_network.in_keys, strict=False).expand(
self.num_qvalue_nets,
*tensordict.batch_size,
)
# preditcted current qvalues
current_qvalue = (
self._vmap_qvalue_network00(
qval_td,
self.qvalue_network_params,
)
.get(self.tensor_keys.state_action_value)
.squeeze(-1)
)
# compute target values for the qvalue loss (reward + gamma * next_target_qvalue * (1 - done))
target_value = self.value_estimator.value_estimate(tensordict).squeeze(-1)
td_error = (current_qvalue - target_value).pow(2)
loss_qval = distance_loss(
current_qvalue,
target_value.expand_as(current_qvalue),
loss_function=self.loss_function,
).sum(0)
metadata = {
"td_error": td_error,
"next_state_value": next_target_qvalue.detach(),
"pred_value": current_qvalue.detach(),
"target_value": target_value.detach(),
}
loss_qval = _reduce(loss_qval, reduction=self.reduction)
return loss_qval, metadata
[docs] @dispatch
def forward(self, tensordict: TensorDictBase) -> TensorDictBase:
"""The forward method.
Computes successively the :meth:`~.actor_loss`, :meth:`~.qvalue_loss`, and returns
a tensordict with these values.
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.
"""
tensordict_save = tensordict
loss_actor, metadata_actor = self.actor_loss(tensordict)
loss_qval, metadata_value = self.qvalue_loss(tensordict_save)
tensordict_save.set(
self.tensor_keys.priority, metadata_value.pop("td_error").detach().max(0)[0]
)
if not loss_qval.shape == loss_actor.shape:
raise RuntimeError(
f"QVal and actor loss have different shape: {loss_qval.shape} and {loss_actor.shape}"
)
td_out = TensorDict(
source={
"loss_actor": loss_actor,
"loss_qvalue": loss_qval,
**metadata_actor,
**metadata_value,
},
batch_size=[],
)
return td_out
[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))
if hasattr(self, "gamma"):
hp["gamma"] = self.gamma
hp.update(hyperparams)
# we do not need a value network bc the next state value is already passed
if value_type == ValueEstimators.TD1:
self._value_estimator = TD1Estimator(value_network=None, **hp)
elif value_type == ValueEstimators.TD0:
self._value_estimator = TD0Estimator(value_network=None, **hp)
elif value_type == ValueEstimators.GAE:
raise NotImplementedError(
f"Value type {value_type} it not implemented for loss {type(self)}."
)
elif value_type == ValueEstimators.TDLambda:
self._value_estimator = TDLambdaEstimator(value_network=None, **hp)
else:
raise NotImplementedError(f"Unknown value type {value_type}")
tensor_keys = {
"value": self.tensor_keys.state_action_value,
"reward": self.tensor_keys.reward,
"done": self.tensor_keys.done,
"terminated": self.tensor_keys.terminated,
}
self._value_estimator.set_keys(**tensor_keys)
