Source code for torchrl.objectives.dreamer
# 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
import torch
from tensordict import TensorDict
from tensordict.nn import TensorDictModule
from tensordict.utils import NestedKey
from torchrl._utils import timeit
from torchrl.envs.model_based.dreamer import DreamerEnv
from torchrl.envs.utils import ExplorationType, set_exploration_type, step_mdp
from torchrl.objectives.common import LossModule
from torchrl.objectives.utils import (
_GAMMA_LMBDA_DEPREC_ERROR,
default_value_kwargs,
distance_loss,
hold_out_net,
ValueEstimators,
) # distance_loss,
from torchrl.objectives.value import TD0Estimator, TD1Estimator, TDLambdaEstimator
[docs]class DreamerModelLoss(LossModule):
"""Dreamer Model Loss.
Computes the loss of the dreamer world model. The loss is composed of the
kl divergence between the prior and posterior of the RSSM,
the reconstruction loss over the reconstructed observation and the reward
loss over the predicted reward.
Reference: https://arxiv.org/abs/1912.01603.
Args:
world_model (TensorDictModule): the world model.
lambda_kl (:obj:`float`, optional): the weight of the kl divergence loss. Default: 1.0.
lambda_reco (:obj:`float`, optional): the weight of the reconstruction loss. Default: 1.0.
lambda_reward (:obj:`float`, optional): the weight of the reward loss. Default: 1.0.
reco_loss (str, optional): the reconstruction loss. Default: "l2".
reward_loss (str, optional): the reward loss. Default: "l2".
free_nats (int, optional): the free nats. Default: 3.
delayed_clamp (bool, optional): if ``True``, the KL clamping occurs after
averaging. If False (default), the kl divergence is clamped to the
free nats value first and then averaged.
global_average (bool, optional): if ``True``, the losses will be averaged
over all dimensions. Otherwise, a sum will be performed over all
non-batch/time dimensions and an average over batch and time.
Default: False.
"""
@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:
reward (NestedKey): The reward is expected to be in the tensordict
key ("next", reward). Defaults to ``"reward"``.
true_reward (NestedKey): The `true_reward` will be stored in the
tensordict key ("next", true_reward). Defaults to ``"true_reward"``.
prior_mean (NestedKey): The prior mean is expected to be in the
tensordict key ("next", prior_mean). Defaults to ``"prior_mean"``.
prior_std (NestedKey): The prior mean is expected to be in the
tensordict key ("next", prior_mean). Defaults to ``"prior_mean"``.
posterior_mean (NestedKey): The posterior mean is expected to be in
the tensordict key ("next", prior_mean). Defaults to ``"posterior_mean"``.
posterior_std (NestedKey): The posterior std is expected to be in
the tensordict key ("next", prior_mean). Defaults to ``"posterior_std"``.
pixels (NestedKey): The pixels is expected to be in the tensordict key ("next", pixels).
Defaults to ``"pixels"``.
reco_pixels (NestedKey): The reconstruction pixels is expected to be
in the tensordict key ("next", reco_pixels). Defaults to ``"reco_pixels"``.
"""
reward: NestedKey = "reward"
true_reward: NestedKey = "true_reward"
prior_mean: NestedKey = "prior_mean"
prior_std: NestedKey = "prior_std"
posterior_mean: NestedKey = "posterior_mean"
posterior_std: NestedKey = "posterior_std"
pixels: NestedKey = "pixels"
reco_pixels: NestedKey = "reco_pixels"
tensor_keys: _AcceptedKeys
default_keys = _AcceptedKeys
decoder: TensorDictModule
reward_model: TensorDictModule
world_mdel: TensorDictModule
def __init__(
self,
world_model: TensorDictModule,
*,
lambda_kl: float = 1.0,
lambda_reco: float = 1.0,
lambda_reward: float = 1.0,
reco_loss: str | None = None,
reward_loss: str | None = None,
free_nats: int = 3,
delayed_clamp: bool = False,
global_average: bool = False,
):
super().__init__()
self.world_model = world_model
self.reco_loss = reco_loss if reco_loss is not None else "l2"
self.reward_loss = reward_loss if reward_loss is not None else "l2"
self.lambda_kl = lambda_kl
self.lambda_reco = lambda_reco
self.lambda_reward = lambda_reward
self.free_nats = free_nats
self.delayed_clamp = delayed_clamp
self.global_average = global_average
self.__dict__["decoder"] = self.world_model[0][-1]
self.__dict__["reward_model"] = self.world_model[1]
def _forward_value_estimator_keys(self, **kwargs) -> None:
pass
[docs] def forward(self, tensordict: TensorDict) -> torch.Tensor:
tensordict = tensordict.clone(recurse=False)
tensordict.rename_key_(
("next", self.tensor_keys.reward),
("next", self.tensor_keys.true_reward),
)
tensordict = self.world_model(tensordict)
# compute model loss
kl_loss = self.kl_loss(
tensordict.get(("next", self.tensor_keys.prior_mean)),
tensordict.get(("next", self.tensor_keys.prior_std)),
tensordict.get(("next", self.tensor_keys.posterior_mean)),
tensordict.get(("next", self.tensor_keys.posterior_std)),
).unsqueeze(-1)
reco_loss = distance_loss(
tensordict.get(("next", self.tensor_keys.pixels)),
tensordict.get(("next", self.tensor_keys.reco_pixels)),
self.reco_loss,
)
if not self.global_average:
reco_loss = reco_loss.sum((-3, -2, -1))
reco_loss = reco_loss.mean().unsqueeze(-1)
reward_loss = distance_loss(
tensordict.get(("next", self.tensor_keys.true_reward)),
tensordict.get(("next", self.tensor_keys.reward)),
self.reward_loss,
)
if not self.global_average:
reward_loss = reward_loss.squeeze(-1)
reward_loss = reward_loss.mean().unsqueeze(-1)
# import ipdb; ipdb.set_trace()
td_out = TensorDict(
loss_model_kl=self.lambda_kl * kl_loss,
loss_model_reco=self.lambda_reco * reco_loss,
loss_model_reward=self.lambda_reward * reward_loss,
)
self._clear_weakrefs(tensordict, td_out)
return (
td_out,
tensordict.detach(),
)
@staticmethod
def normal_log_probability(x, mean, std):
return (
-0.5 * ((x.to(mean.dtype) - mean) / std).pow(2) - std.log()
) # - 0.5 * math.log(2 * math.pi)
def kl_loss(
self,
prior_mean: torch.Tensor,
prior_std: torch.Tensor,
posterior_mean: torch.Tensor,
posterior_std: torch.Tensor,
) -> torch.Tensor:
kl = (
torch.log(prior_std / posterior_std)
+ (posterior_std**2 + (prior_mean - posterior_mean) ** 2)
/ (2 * prior_std**2)
- 0.5
)
if not self.global_average:
kl = kl.sum(-1)
if self.delayed_clamp:
kl = kl.mean().clamp_min(self.free_nats)
else:
kl = kl.clamp_min(self.free_nats).mean()
return kl
[docs]class DreamerActorLoss(LossModule):
"""Dreamer Actor Loss.
Computes the loss of the dreamer actor. The actor loss is computed as the
negative average lambda return.
Reference: https://arxiv.org/abs/1912.01603.
Args:
actor_model (TensorDictModule): the actor model.
value_model (TensorDictModule): the value model.
model_based_env (DreamerEnv): the model based environment.
imagination_horizon (int, optional): The number of steps to unroll the
model. Defaults to ``15``.
discount_loss (bool, optional): if ``True``, the loss is discounted with a
gamma discount factor. Default to ``False``.
"""
@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:
belief (NestedKey): The input tensordict key where the belief is expected.
Defaults to ``"belief"``.
reward (NestedKey): The reward is expected to be in the tensordict key ("next", reward).
Defaults to ``"reward"``.
value (NestedKey): The reward is expected to be in the tensordict key ("next", value).
Will be used for the underlying value estimator. Defaults to ``"state_value"``.
done (NestedKey): The input tensordict key where the flag if a
trajectory is done is expected ("next", done). Defaults to ``"done"``.
terminated (NestedKey): The input tensordict key where the flag if a
trajectory is terminated is expected ("next", terminated). Defaults to ``"terminated"``.
"""
belief: NestedKey = "belief"
reward: NestedKey = "reward"
value: NestedKey = "state_value"
done: NestedKey = "done"
terminated: NestedKey = "terminated"
tensor_keys: _AcceptedKeys
default_keys = _AcceptedKeys
default_value_estimator = ValueEstimators.TDLambda
value_model: TensorDictModule
actor_model: TensorDictModule
def __init__(
self,
actor_model: TensorDictModule,
value_model: TensorDictModule,
model_based_env: DreamerEnv,
*,
imagination_horizon: int = 15,
discount_loss: bool = True, # for consistency with paper
gamma: int | None = None,
lmbda: int | None = None,
):
super().__init__()
self.actor_model = actor_model
self.__dict__["value_model"] = value_model
self.model_based_env = model_based_env
self.imagination_horizon = imagination_horizon
self.discount_loss = discount_loss
if gamma is not None:
raise TypeError(_GAMMA_LMBDA_DEPREC_ERROR)
if lmbda is not None:
raise TypeError(_GAMMA_LMBDA_DEPREC_ERROR)
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,
)
[docs] def forward(self, tensordict: TensorDict) -> tuple[TensorDict, TensorDict]:
tensordict = tensordict.select("state", self.tensor_keys.belief).detach()
with timeit("actor_loss/time-rollout"), hold_out_net(
self.model_based_env
), set_exploration_type(ExplorationType.RANDOM):
tensordict = self.model_based_env.reset(tensordict.copy())
fake_data = self.model_based_env.rollout(
max_steps=self.imagination_horizon,
policy=self.actor_model,
auto_reset=False,
tensordict=tensordict,
)
next_tensordict = step_mdp(fake_data, keep_other=True)
with hold_out_net(self.value_model):
next_tensordict = self.value_model(next_tensordict)
reward = fake_data.get(("next", self.tensor_keys.reward))
next_value = next_tensordict.get(self.tensor_keys.value)
lambda_target = self.lambda_target(reward, next_value)
fake_data.set("lambda_target", lambda_target)
if self.discount_loss:
gamma = self.value_estimator.gamma.to(tensordict.device)
discount = gamma.expand(lambda_target.shape).clone()
discount[..., 0, :] = 1
discount = discount.cumprod(dim=-2)
actor_loss = -(lambda_target * discount).sum((-2, -1)).mean()
else:
actor_loss = -lambda_target.sum((-2, -1)).mean()
loss_tensordict = TensorDict({"loss_actor": actor_loss}, [])
self._clear_weakrefs(tensordict, loss_tensordict)
return loss_tensordict, fake_data.detach()
def lambda_target(self, reward: torch.Tensor, value: torch.Tensor) -> torch.Tensor:
done = torch.zeros(reward.shape, dtype=torch.bool, device=reward.device)
terminated = torch.zeros(reward.shape, dtype=torch.bool, device=reward.device)
input_tensordict = TensorDict(
{
("next", self.tensor_keys.reward): reward,
("next", self.tensor_keys.value): value,
("next", self.tensor_keys.done): done,
("next", self.tensor_keys.terminated): terminated,
},
[],
)
return self.value_estimator.value_estimate(input_tensordict)
[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))
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:
if hasattr(self, "lmbda"):
hp["lmbda"] = self.lmbda
raise NotImplementedError(
f"Value type {value_type} it not implemented for loss {type(self)}."
)
elif value_type is ValueEstimators.TDLambda:
if hasattr(self, "lmbda"):
hp["lmbda"] = self.lmbda
self._value_estimator = TDLambdaEstimator(
**hp,
value_network=value_net,
vectorized=True, # TODO: vectorized version seems not to be similar to the non vectorised
)
else:
raise NotImplementedError(f"Unknown value type {value_type}")
tensor_keys = {
"value": self.tensor_keys.value,
"value_target": "value_target",
}
self._value_estimator.set_keys(**tensor_keys)
[docs]class DreamerValueLoss(LossModule):
"""Dreamer Value Loss.
Computes the loss of the dreamer value model. The value loss is computed
between the predicted value and the lambda target.
Reference: https://arxiv.org/abs/1912.01603.
Args:
value_model (TensorDictModule): the value model.
value_loss (str, optional): the loss to use for the value loss.
Default: ``"l2"``.
discount_loss (bool, optional): if ``True``, the loss is discounted with a
gamma discount factor. Default: False.
gamma (:obj:`float`, optional): the gamma discount factor. Default: ``0.99``.
"""
@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.
Defaults to ``"state_value"``.
"""
value: NestedKey = "state_value"
tensor_keys: _AcceptedKeys
default_keys = _AcceptedKeys
value_model: TensorDictModule
def __init__(
self,
value_model: TensorDictModule,
value_loss: str | None = None,
discount_loss: bool = True, # for consistency with paper
gamma: int = 0.99,
):
super().__init__()
self.value_model = value_model
self.value_loss = value_loss if value_loss is not None else "l2"
self.gamma = gamma
self.discount_loss = discount_loss
def _forward_value_estimator_keys(self, **kwargs) -> None:
pass
[docs] def forward(self, fake_data) -> torch.Tensor:
lambda_target = fake_data.get("lambda_target")
tensordict_select = fake_data.select(*self.value_model.in_keys, strict=False)
self.value_model(tensordict_select)
if self.discount_loss:
discount = self.gamma * torch.ones_like(
lambda_target, device=lambda_target.device
)
discount[..., 0, :] = 1
discount = discount.cumprod(dim=-2)
value_loss = (
(
discount
* distance_loss(
tensordict_select.get(self.tensor_keys.value),
lambda_target,
self.value_loss,
)
)
.sum((-1, -2))
.mean()
)
else:
value_loss = (
distance_loss(
tensordict_select.get(self.tensor_keys.value),
lambda_target,
self.value_loss,
)
.sum((-1, -2))
.mean()
)
loss_tensordict = TensorDict({"loss_value": value_loss})
self._clear_weakrefs(fake_data, loss_tensordict)
return loss_tensordict, fake_data
