TD3Loss

class torchrl.objectives.TD3Loss(*args, **kwargs)

TD3 Loss module.

Parameters:

• 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 Arguments:

• 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 10.

• policy_noise (float, optional) – Standard deviation for the target policy action noise. Default is 0.2.

• noise_clip (float, optional) – Clipping range value for the sampled target policy action noise. Default is 0.5.

• 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 BoundedTensorSpec
>>> 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 import TD3Loss
>>> from tensordict import TensorDict
>>> n_act, n_obs = 4, 3
>>> spec = BoundedTensorSpec(-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 = TD3Loss(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={
        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([]), device=cpu, dtype=torch.float32, is_shared=False),
        pred_value: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
        state_action_value_actor: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.float32, is_shared=False),
        target_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 and qvalue network The return value is a tuple of tensors in the following order: ["loss_actor", "loss_qvalue", "pred_value", "state_action_value_actor", "next_state_value", "target_value",].

Examples

>>> import torch
>>> from torch import nn
>>> from torchrl.data import BoundedTensorSpec
>>> from torchrl.modules.tensordict_module.actors import Actor, ValueOperator
>>> from torchrl.objectives.td3 import TD3Loss
>>> n_act, n_obs = 4, 3
>>> spec = BoundedTensorSpec(-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 = TD3Loss(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()

forward(tensordict: TensorDictBase) → TensorDictBase

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.

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

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)