IQLLoss¶

class torchrl.objectives.IQLLoss(*args, **kwargs)[source]¶

TorchRL implementation of the IQL loss.

Presented in “Offline Reinforcement Learning with Implicit Q-Learning” https://arxiv.org/abs/2110.06169

Parameters:

actor_network (ProbabilisticActor) – stochastic actor
qvalue_network (TensorDictModule) – Q(s, a) parametric model
value_network (TensorDictModule, optional) – V(s) parametric model.

Keyword Arguments:

num_qvalue_nets (integer, optional) – number of Q-Value networks used. Defaults to 2.
loss_function (str, optional) – loss function to be used with the value function loss. Default is “smooth_l1”.
temperature (float, optional) – Inverse temperature (beta). For smaller hyperparameter values, the objective behaves similarly to behavioral cloning, while for larger values, it attempts to recover the maximum of the Q-function.
expectile (float, optional) – expectile \(\tau\). A larger value of \(\tau\) is crucial for antmaze tasks that require dynamical programming (“stichting”).
priority_key (str, optional) – [Deprecated, use .set_keys(priority_key=priority_key) instead] tensordict key where to write the priority (for prioritized replay buffer usage). Default is “td_error”.
separate_losses (bool, optional) – if True, shared parameters between policy and critic will only be trained on the policy loss. Defaults to False, ie. 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.continuous import NormalParamWrapper, TanhNormal
>>> from torchrl.modules.tensordict_module.actors import ProbabilisticActor, ValueOperator
>>> from torchrl.modules.tensordict_module.common import SafeModule
>>> from torchrl.objectives.iql import IQLLoss
>>> from tensordict import TensorDict
>>> n_act, n_obs = 4, 3
>>> spec = BoundedTensorSpec(-torch.ones(n_act), torch.ones(n_act), (n_act,))
>>> net = NormalParamWrapper(nn.Linear(n_obs, 2 * n_act))
>>> module = SafeModule(net, in_keys=["observation"], out_keys=["loc", "scale"])
>>> actor = ProbabilisticActor(
...     module=module,
...     in_keys=["loc", "scale"],
...     spec=spec,
...     distribution_class=TanhNormal)
>>> class QValueClass(nn.Module):
...     def __init__(self):
...         super().__init__()
...         self.linear = nn.Linear(n_obs + n_act, 1)
...     def forward(self, obs, act):
...         return self.linear(torch.cat([obs, act], -1))
>>> qvalue = SafeModule(
...     QValueClass(),
...     in_keys=["observation", "action"],
...     out_keys=["state_action_value"],
... )
>>> value = SafeModule(
...     nn.Linear(n_obs, 1),
...     in_keys=["observation"],
...     out_keys=["state_value"],
... )
>>> loss = IQLLoss(actor, qvalue, value)
>>> batch = [2, ]
>>> action = spec.rand(batch)
>>> data = TensorDict({
...         "observation": torch.randn(*batch, n_obs),
...         "action": action,
...         ("next", "done"): torch.zeros(*batch, 1, dtype=torch.bool),
...         ("next", "terminated"): torch.zeros(*batch, 1, dtype=torch.bool),
...         ("next", "reward"): torch.randn(*batch, 1),
...         ("next", "observation"): torch.randn(*batch, n_obs),
...     }, batch)
>>> loss(data)
TensorDict(
    fields={
        entropy: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
        loss_actor: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
        loss_qvalue: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
        loss_value: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([]),
    device=None,
    is_shared=False)

This class is compatible with non-tensordict based modules too and can be used without recurring to any tensordict-related primitive. In this case, the expected keyword arguments are: ["action", "next_reward", "next_done", "next_terminated"] + in_keys of the actor, value, and qvalue network The return value is a tuple of tensors in the following order: ["loss_actor", "loss_qvalue", "loss_value", "entropy"].

Examples

>>> import torch
>>> from torch import nn
>>> from torchrl.data import BoundedTensorSpec
>>> from torchrl.modules.distributions.continuous import NormalParamWrapper, TanhNormal
>>> from torchrl.modules.tensordict_module.actors import ProbabilisticActor, ValueOperator
>>> from torchrl.modules.tensordict_module.common import SafeModule
>>> from torchrl.objectives.iql import IQLLoss
>>> _ = torch.manual_seed(42)
>>> n_act, n_obs = 4, 3
>>> spec = BoundedTensorSpec(-torch.ones(n_act), torch.ones(n_act), (n_act,))
>>> net = NormalParamWrapper(nn.Linear(n_obs, 2 * n_act))
>>> module = SafeModule(net, in_keys=["observation"], out_keys=["loc", "scale"])
>>> actor = ProbabilisticActor(
...     module=module,
...     in_keys=["loc", "scale"],
...     spec=spec,
...     distribution_class=TanhNormal)
>>> class QValueClass(nn.Module):
...     def __init__(self):
...         super().__init__()
...         self.linear = nn.Linear(n_obs + n_act, 1)
...     def forward(self, obs, act):
...         return self.linear(torch.cat([obs, act], -1))
>>> qvalue = SafeModule(
...     QValueClass(),
...     in_keys=["observation", "action"],
...     out_keys=["state_action_value"],
... )
>>> value = SafeModule(
...     nn.Linear(n_obs, 1),
...     in_keys=["observation"],
...     out_keys=["state_value"],
... )
>>> loss = IQLLoss(actor, qvalue, value)
>>> batch = [2, ]
>>> action = spec.rand(batch)
>>> loss_actor, loss_qvalue, loss_value, entropy = loss(
...     observation=torch.randn(*batch, n_obs),
...     action=action,
...     next_done=torch.zeros(*batch, 1, dtype=torch.bool),
...     next_terminated=torch.zeros(*batch, 1, dtype=torch.bool),
...     next_observation=torch.zeros(*batch, n_obs),
...     next_reward=torch.randn(*batch, 1))
>>> loss_actor.backward()

The output keys can also be filtered using the IQLLoss.select_out_keys() method.

Examples

>>> _ = loss.select_out_keys('loss_actor', 'loss_qvalue')
>>> loss_actor, loss_qvalue = loss(
...     observation=torch.randn(*batch, n_obs),
...     action=action,
...     next_done=torch.zeros(*batch, 1, dtype=torch.bool),
...     next_terminated=torch.zeros(*batch, 1, dtype=torch.bool),
...     next_observation=torch.zeros(*batch, n_obs),
...     next_reward=torch.randn(*batch, 1))
>>> loss_actor.backward()

forward(tensordict: TensorDictBase) → TensorDictBase[source]¶

It is designed to read an input TensorDict and return another tensordict with loss keys named “loss*”.

Splitting the loss in its component can then be used by the trainer to log the various loss values throughout training. Other scalars present in the output tensordict will be logged too.

Parameters:: tensordict – an input tensordict with the values required to compute the loss.
Returns:: A new tensordict with no batch dimension containing various loss scalars which will be named “loss*”. It is essential that the losses are returned with this name as they will be read by the trainer before backpropagation.

static loss_value_diff(diff, expectile=0.8)[source]¶: Loss function for iql expectile value difference.

make_value_estimator(value_type: Optional[ValueEstimators] = None, **hyperparams)[source]¶

Value-function constructor.

If the non-default value function is wanted, it must be built using this method.

Parameters:

value_type (ValueEstimators) – A ValueEstimators enum type indicating the value function to use. If none is provided, the default stored in the default_value_estimator attribute will be used. The resulting value estimator class will be registered in self.value_type, allowing future refinements.
**hyperparams – hyperparameters to use for the value function. If not provided, the value indicated by default_value_kwargs() will be used.

Examples

>>> from torchrl.objectives import DQNLoss
>>> # initialize the DQN loss
>>> actor = torch.nn.Linear(3, 4)
>>> dqn_loss = DQNLoss(actor, action_space="one-hot")
>>> # updating the parameters of the default value estimator
>>> dqn_loss.make_value_estimator(gamma=0.9)
>>> dqn_loss.make_value_estimator(
...     ValueEstimators.TD1,
...     gamma=0.9)
>>> # if we want to change the gamma value
>>> dqn_loss.make_value_estimator(dqn_loss.value_type, gamma=0.9)

IQLLoss¶

Docs

Tutorials

Resources