ActorCriticOperator

class torchrl.modules.tensordict_module.ActorCriticOperator(*args, **kwargs)

Actor-critic operator.

This class wraps together an actor and a value model that share a common observation embedding network:

Note

For a similar class that returns an action and a state-value \(V(s)\) see ActorValueOperator.

To facilitate the workflow, this class comes with a get_policy_operator() method, which will both return a standalone TDModule with the dedicated functionality. The get_critic_operator will return the parent object, as the value is computed based on the policy output.

Parameters:

• common_operator (TensorDictModule) – a common operator that reads observations and produces a hidden variable

• policy_operator (TensorDictModule) – a policy operator that reads the hidden variable and returns an action

• value_operator (TensorDictModule) – a value operator, that reads the hidden variable and returns a value

Examples

>>> import torch
>>> from tensordict import TensorDict
>>> from torchrl.modules import ProbabilisticActor
>>> from torchrl.modules import  ValueOperator, TanhNormal, ActorCriticOperator, NormalParamWrapper, MLP
>>> module_hidden = torch.nn.Linear(4, 4)
>>> td_module_hidden = SafeModule(
...    module=module_hidden,
...    in_keys=["observation"],
...    out_keys=["hidden"],
...    )
>>> module_action = NormalParamWrapper(torch.nn.Linear(4, 8))
>>> module_action = TensorDictModule(module_action, in_keys=["hidden"], out_keys=["loc", "scale"])
>>> td_module_action = ProbabilisticActor(
...    module=module_action,
...    in_keys=["loc", "scale"],
...    out_keys=["action"],
...    distribution_class=TanhNormal,
...    return_log_prob=True,
...    )
>>> module_value = MLP(in_features=8, out_features=1, num_cells=[])
>>> td_module_value = ValueOperator(
...    module=module_value,
...    in_keys=["hidden", "action"],
...    out_keys=["state_action_value"],
...    )
>>> td_module = ActorCriticOperator(td_module_hidden, td_module_action, td_module_value)
>>> td = TensorDict({"observation": torch.randn(3, 4)}, [3,])
>>> td_clone = td_module(td.clone())
>>> print(td_clone)
TensorDict(
    fields={
        action: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        hidden: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        loc: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        observation: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        sample_log_prob: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False),
        scale: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        state_action_value: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([3]),
    device=None,
    is_shared=False)
>>> td_clone = td_module.get_policy_operator()(td.clone())
>>> print(td_clone)  # no value
TensorDict(
    fields={
        action: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        hidden: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        loc: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        observation: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        sample_log_prob: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False),
        scale: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([3]),
    device=None,
    is_shared=False)
>>> td_clone = td_module.get_critic_operator()(td.clone())
>>> print(td_clone)  # no action
TensorDict(
    fields={
        action: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        hidden: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        loc: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        observation: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        sample_log_prob: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False),
        scale: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False),
        state_action_value: Tensor(shape=torch.Size([3, 1]), device=cpu, dtype=torch.float32, is_shared=False)},
    batch_size=torch.Size([3]),
    device=None,
    is_shared=False)

get_critic_operator() → TensorDictModuleWrapper

Returns a standalone critic network operator that maps a state-action pair to a critic estimate.

get_policy_head() → SafeSequential

Returns the policy head.

get_value_head() → SafeSequential

Returns the value head.

get_value_operator() → TensorDictModuleWrapper

Returns a standalone value network operator that maps an observation to a value estimate.