class torchrl.modules.tensordict_module.ProbabilisticActor(*args, **kwargs)[source]

General class for probabilistic actors in RL.

The Actor class comes with default values for the out_keys ([“action”]) and if the spec is provided but not as a CompositeSpec object, it will be automatically translated into spec = CompositeSpec(action=spec)

  • module (nn.Module) – a torch.nn.Module used to map the input to the output parameter space.

  • in_keys (str or iterable of str or dict) – key(s) that will be read from the input TensorDict and used to build the distribution. Importantly, if it’s an iterable of string or a string, those keys must match the keywords used by the distribution class of interest, e.g. "loc" and "scale" for the Normal distribution and similar. If in_keys is a dictionary,, the keys are the keys of the distribution and the values are the keys in the tensordict that will get match to the corresponding distribution keys.

  • out_keys (str or iterable of str) – keys where the sampled values will be written. Importantly, if these keys are found in the input TensorDict, the sampling step will be skipped.

  • spec (TensorSpec, optional) – keyword-only argument containing the specs of the output tensor. If the module outputs multiple output tensors, spec characterize the space of the first output tensor.

  • safe (bool) – keyword-only argument. if True, the value of the output is checked against the input spec. Out-of-domain sampling can occur because of exploration policies or numerical under/overflow issues. If this value is out of bounds, it is projected back onto the desired space using the TensorSpec.project method. Default is False.

  • default_interaction_type (str, optional) –

    keyword-only argument. Default method to be used to retrieve the output value. Should be one of: ‘InteractionType.MODE’, ‘InteractionType.MEDIAN’, ‘InteractionType.MEAN’ or ‘InteractionType.RANDOM’ (in which case the value is sampled randomly from the distribution). Defaults to is ‘InteractionType.RANDOM’.


    When a sample is drawn, the ProbabilisticActor instance will first look for the interaction mode dictated by the interaction_type() global function. If this returns None (its default value), then the default_interaction_type of the ProbabilisticTDModule instance will be used. Note that DataCollectorBase instances will use set_interaction_type to tensordict.nn.InteractionType.RANDOM by default.

  • distribution_class (Type, optional) – keyword-only argument. A torch.distributions.Distribution class to be used for sampling. Default is tensordict.nn.distributions.Delta.

  • distribution_kwargs (dict, optional) – keyword-only argument. Keyword-argument pairs to be passed to the distribution.

  • return_log_prob (bool, optional) – keyword-only argument. If True, the log-probability of the distribution sample will be written in the tensordict with the key ‘sample_log_prob’. Default is False.

  • cache_dist (bool, optional) – keyword-only argument. EXPERIMENTAL: if True, the parameters of the distribution (i.e. the output of the module) will be written to the tensordict along with the sample. Those parameters can be used to re-compute the original distribution later on (e.g. to compute the divergence between the distribution used to sample the action and the updated distribution in PPO). Default is False.

  • n_empirical_estimate (int, optional) – keyword-only argument. Number of samples to compute the empirical mean when it is not available. Defaults to 1000.


>>> import torch
>>> from tensordict import TensorDict
>>> from tensordict.nn import TensorDictModule
>>> from import BoundedTensorSpec
>>> from torchrl.modules import ProbabilisticActor, NormalParamWrapper, TanhNormal
>>> td = TensorDict({"observation": torch.randn(3, 4)}, [3,])
>>> action_spec = BoundedTensorSpec(shape=torch.Size([4]),
...    low=-1, high=1)
>>> module = NormalParamWrapper(torch.nn.Linear(4, 8))
>>> tensordict_module = TensorDictModule(module, in_keys=["observation"], out_keys=["loc", "scale"])
>>> td_module = ProbabilisticActor(
...    module=tensordict_module,
...    spec=action_spec,
...    in_keys=["loc", "scale"],
...    distribution_class=TanhNormal,
...    )
>>> td = td_module(td)
>>> td
        action: 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),
        scale: Tensor(shape=torch.Size([3, 4]), device=cpu, dtype=torch.float32, is_shared=False)},

Probabilistic actors also support compound actions through the tensordict.nn.CompositeDistribution class. This distribution takes a tensordict as input (typically “params”) and reads it as a whole: the content of this tensordict is the input to the distributions contained in the compound one.


>>> from tensordict import TensorDict
>>> from tensordict.nn import CompositeDistribution, TensorDictModule
>>> from torchrl.modules import ProbabilisticActor
>>> from torch import nn, distributions as d
>>> import torch
>>> class Module(nn.Module):
...     def forward(self, x):
...         return x[..., :3], x[..., 3:6], x[..., 6:]
>>> module = TensorDictModule(Module(),
...                           in_keys=["x"],
...                           out_keys=[("params", "normal", "loc"),
...                              ("params", "normal", "scale"),
...                              ("params", "categ", "logits")])
>>> actor = ProbabilisticActor(module,
...                            in_keys=["params"],
...                            distribution_class=CompositeDistribution,
...                            distribution_kwargs={"distribution_map": {
...                                 "normal": d.Normal, "categ": d.Categorical}}
...                           )
>>> data = TensorDict({"x": torch.rand(10)}, [])
>>> actor(data)
        categ: Tensor(shape=torch.Size([]), device=cpu, dtype=torch.int64, is_shared=False),
        normal: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False),
        params: TensorDict(
                categ: TensorDict(
                        logits: Tensor(shape=torch.Size([4]), device=cpu, dtype=torch.float32, is_shared=False)},
                normal: TensorDict(
                        loc: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False),
                        scale: Tensor(shape=torch.Size([3]), device=cpu, dtype=torch.float32, is_shared=False)},
        x: Tensor(shape=torch.Size([10]), device=cpu, dtype=torch.float32, is_shared=False)},


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