reference/generated/torchrl.objectives.value.GAE

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GAE

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

A class wrapper around the generalized advantage estimate functional.

Refer to “HIGH-DIMENSIONAL CONTINUOUS CONTROL USING GENERALIZED ADVANTAGE ESTIMATION” https://arxiv.org/pdf/1506.02438.pdf for more context.

Parameters:

• gamma (scalar) – exponential mean discount.

• lmbda (scalar) – trajectory discount.

• value_network (TensorDictModule, optional) – value operator used to retrieve the value estimates. If None, this module will expect the "state_value" keys to be already filled, and will not call the value network to produce it.

• average_gae (bool) – if True, the resulting GAE values will be standardized. Default is False.

• 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 if not compiling.

• 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. Defaults to “state_value”.

• 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.

• auto_reset_env (bool, optional) – if True, the last "next" state of the episode isn’t valid, so the GAE calculation will use the value instead of next_value to bootstrap truncated episodes.

• deactivate_vmap (bool, optional) – if True, no vmap call will be used, and vectorized maps will be replaced with simple for loops. 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.

GAE will return an "advantage" entry containing the advantage value. It will also return a "value_target" entry with the return value that is to be used to train the value network. Finally, if gradient_mode is True, an additional and differentiable "value_error" entry will be returned, which simply represents the difference between the return and the value network output (i.e. an additional distance loss should be applied to that signed value).

Note

As other advantage functions do, if the value_key is already present in the input tensordict, the GAE module will ignore the calls to the value network (if any) and use the provided value instead.

Note

GAE can be used with value networks that rely on recurrent neural networks, provided that the init markers (“is_init”) and terminated / truncated markers are properly set. With shifted=True, reset next-observations are inserted into a fixed-shape value-network call according to shifted_budget. If shifted=False, the root and "next" trajectories are stacked and the value network is called with vmap over the stack of trajectories. Because RNNs require a fair amount of control flow, they are currently not compatible with torch.vmap and, as such, the deactivate_vmap option must be turned on in these cases. Similarly, if shifted=False, the "is_init" entry of the root tensordict will be copied onto the "is_init" of the "next" entry, such that trajectories are well separated both for root and "next" data.

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

Computes the GAE 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 GAE. 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.

• 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. Negative dimensions are considered with respect to the input tensordict.

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 = GAE(
...     gamma=0.98,
...     lmbda=0.94,
...     value_network=value_net,
...     differentiable=False,
... )
>>> 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 = GAE(
...     gamma=0.98,
...     lmbda=0.94,
...     value_network=value_net,
...     differentiable=False,
... )
>>> 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, params: TensorDictBase | None = None, target_params: TensorDictBase | 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.