SACLoss

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

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

Parameters:

• 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 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”.

• 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 -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 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()

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.

load_state_dict(state_dict: Mapping[str, Any], strict: bool = True, assign: bool = False)

Copy parameters and buffers from state_dict into this module and its descendants.

If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.

Warning

If assign is True the optimizer must be created after the call to load_state_dict unless get_swap_module_params_on_conversion() is True.

Parameters:

• state_dict (dict) – a dict containing parameters and persistent buffers.

• strict (bool, optional) – whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: True

• assign (bool, optional) – When False, the properties of the tensors in the current module are preserved while when True, the properties of the Tensors in the state dict are preserved. The only exception is the requires_grad field of Default: ``False`

Returns:

• missing_keys is a list of str containing any keys that are expected

  by this module but missing from the provided state_dict.

• unexpected_keys is a list of str containing the keys that are not

  expected by this module but present in the provided state_dict.

Return type:

NamedTuple with missing_keys and unexpected_keys fields

Note

If a parameter or buffer is registered as None and its corresponding key exists in state_dict, load_state_dict() will raise a RuntimeError.

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)

state_dict(*args, destination=None, prefix='', keep_vars=False)

Return a dictionary containing references to the whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to None are not included.

Note

The returned object is a shallow copy. It contains references to the module’s parameters and buffers.

Warning

Currently state_dict() also accepts positional arguments for destination, prefix and keep_vars in order. However, this is being deprecated and keyword arguments will be enforced in future releases.

Warning

Please avoid the use of argument destination as it is not designed for end-users.

Parameters:

• destination (dict, optional) – If provided, the state of module will be updated into the dict and the same object is returned. Otherwise, an OrderedDict will be created and returned. Default: None.

• prefix (str, optional) – a prefix added to parameter and buffer names to compose the keys in state_dict. Default: ''.

• keep_vars (bool, optional) – by default the Tensor s returned in the state dict are detached from autograd. If it’s set to True, detaching will not be performed. Default: False.

Returns:

a dictionary containing a whole state of the module

Return type:

dict

Example:

>>> # xdoctest: +SKIP("undefined vars")
>>> module.state_dict().keys()
['bias', 'weight']