reference/generated/torchrl.objectives.value.TDLambdaEstimator

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TDLambdaEstimator

class torchrl.objectives.value.TDLambdaEstimator(*args, **kwargs)

TD(\(\lambda\)) estimate of advantage function.

Parameters:

• gamma (scalar) – exponential mean discount.

• lmbda (scalar) – trajectory discount.

• value_network (TensorDictModule) – value operator used to retrieve the value estimates.

• average_rewards (bool, optional) – if True, rewards will be standardized before the TD is computed.

• differentiable (bool, optional) –

  if True, gradients are propagated through the computation of the value function. Default is False.

  Note

  The proper way to make the function call non-differentiable is to decorate it in a torch.no_grad() context manager/decorator or pass detached parameters for functional modules.

• vectorized (bool, optional) – whether to use the vectorized version of the lambda return. Default is True.

• skip_existing (bool, optional) – if True, the value network will skip modules which outputs are already present in the tensordict. Defaults to None, i.e., the value of tensordict.nn.skip_existing() is not affected.

• advantage_key (str or tuple of str, optional) – [Deprecated] the key of the advantage entry. Defaults to "advantage".

• value_target_key (str or tuple of str, optional) – [Deprecated] the key of the advantage entry. Defaults to "value_target".

• value_key (str or tuple of str, optional) – [Deprecated] the value key to read from the input tensordict. Defaults to "state_value".

• shifted (bool, optional) –

  controls how value and next-value are obtained from the value network. False (default) calls the value network twice (once on the root tensordict, once on "next"), which is correct whenever "next" may differ non-trivially from obs[t+1]. Truthy values request a single call:

  • True: fixed-budget single-call path. Inserts the true ("next", <in_key>) entry after every internal truncation (done & ~terminated), shifts subsequent samples to the right inside a sequence of length T + shifted_budget and masks the displaced suffix via "shifted_valid". Terminal steps (done & terminated) do not consume budget. Retained samples use exact next observations.

  Note

  Single-step rollout assumption. shifted=True relies on the standard one-step rollout layout produced by env.step + auto-reset: at every position where done[t] = False, the value-net inputs in ("next", <in_key>)[t] are expected to equal <in_key>[t+1]. The backend uses this invariant to evaluate V once over a fused [T + shifted_budget] sequence instead of twice over [T] streams.

  The canonical pipeline that breaks the invariant is multi-step return processing (MultiStep / n-step bootstrapping), which rewrites ("next", obs)[t] to obs[t+n] with n > 1. shifted=True is unsupported with multi-step returns — use shifted=False instead.

  Single-call paths also require that the parameters at time t and t+1 are identical (i.e. target_params is not used).

  Defaults to False.

• device (torch.device, optional) – the device where the buffers will be instantiated. Defaults to torch.get_default_device().

• time_dim (int, optional) – the dimension corresponding to the time in the input tensordict. If not provided, defaults to the dimension marked with the "time" name if any, and to the last dimension otherwise. Can be overridden during a call to value_estimate(). Negative dimensions are considered with respect to the input tensordict.

• deactivate_vmap (bool, optional) – whether to deactivate vmap calls and replace them with a plain for loop. Defaults to False.

• value_chunk_size (int, optional) – if set, splits value-network calls into chunks of this many elements along the leading dimension. Defaults to None.

• num_chunks (int, optional) – if set, splits value-network calls into this many chunks along the leading dimension. Mutually exclusive with value_chunk_size. num_chunk is accepted as an alias. Defaults to None.

• num_chunk (int, optional) – alias for num_chunks. Cannot be set together with a different num_chunks value. Defaults to None.

• shifted_budget (int, optional) – number of extra value-network time slots used when shifted=True. 1 uses a T+1 budget, 2 can represent one internal reset plus the rollout boundary without dropping samples, and so on. Defaults to 1.

forward(tensordict: TensorDictBase = None, *, params: list[Tensor] | None = None, target_params: list[Tensor] | None = None) → TensorDictBase

Computes the TD(\(\lambda\)) advantage given the data in tensordict.

If a functional module is provided, a nested TensorDict containing the parameters (and if relevant the target parameters) can be passed to the module.

Parameters:

tensordict (TensorDictBase) – A TensorDict containing the data (an observation key, "action", ("next", "reward"), ("next", "done"), ("next", "terminated"), and "next" tensordict state as returned by the environment) necessary to compute the value estimates and the TDLambdaEstimate. The data passed to this module should be structured as [*B, T, *F] where B are the batch size, T the time dimension and F the feature dimension(s). The tensordict must have shape [*B, T].

Keyword Arguments:

• params (TensorDictBase, optional) – A nested TensorDict containing the params to be passed to the functional value network module.

• target_params (TensorDictBase, optional) – A nested TensorDict containing the target params to be passed to the functional value network module.

Returns:

An updated TensorDict with an advantage and a value_error keys as defined in the constructor.

Examples

>>> from tensordict import TensorDict
>>> value_net = TensorDictModule(
...     nn.Linear(3, 1), in_keys=["obs"], out_keys=["state_value"]
... )
>>> module = TDLambdaEstimator(
...     gamma=0.98,
...     lmbda=0.94,
...     value_network=value_net,
... )
>>> obs, next_obs = torch.randn(2, 1, 10, 3)
>>> reward = torch.randn(1, 10, 1)
>>> done = torch.zeros(1, 10, 1, dtype=torch.bool)
>>> terminated = torch.zeros(1, 10, 1, dtype=torch.bool)
>>> tensordict = TensorDict({"obs": obs, "next": {"obs": next_obs, "done": done, "reward": reward, "terminated": terminated}}, [1, 10])
>>> _ = module(tensordict)
>>> assert "advantage" in tensordict.keys()

The module supports non-tensordict (i.e. unpacked tensordict) inputs too:

Examples

>>> value_net = TensorDictModule(
...     nn.Linear(3, 1), in_keys=["obs"], out_keys=["state_value"]
... )
>>> module = TDLambdaEstimator(
...     gamma=0.98,
...     lmbda=0.94,
...     value_network=value_net,
... )
>>> obs, next_obs = torch.randn(2, 1, 10, 3)
>>> reward = torch.randn(1, 10, 1)
>>> done = torch.zeros(1, 10, 1, dtype=torch.bool)
>>> terminated = torch.zeros(1, 10, 1, dtype=torch.bool)
>>> advantage, value_target = module(obs=obs, next_reward=reward, next_done=done, next_obs=next_obs, next_terminated=terminated)

value_estimate(tensordict, target_params: TensorDictBase | None = None, next_value: Tensor | None = None, time_dim: int | None = None, **kwargs)

Gets a value estimate, usually used as a target value for the value network.

If the state value key is present under tensordict.get(("next", self.tensor_keys.value)) then this value will be used without recurring to the value network.

Parameters:

• tensordict (TensorDictBase) – the tensordict containing the data to read.

• target_params (TensorDictBase, optional) – A nested TensorDict containing the target params to be passed to the functional value network module.

• next_value (torch.Tensor, optional) – the value of the next state or state-action pair. Exclusive with target_params.

• **kwargs – the keyword arguments to be passed to the value network.

Returns: a tensor corresponding to the state value.