TD0Estimator

class torchrl.objectives.value.TD0Estimator(*args, **kwargs)

Temporal Difference (TD(0)) estimate of advantage function.

AKA bootstrapped temporal difference or 1-step return.

Keyword Arguments:

• gamma (scalar) – exponential mean discount.

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

• shifted (bool, optional) – if True, the value and next value are estimated with a single call to the value network. This is faster but is only valid whenever (1) the "next" value is shifted by only one time step (which is not the case with multi-step value estimation, for instance) and (2) when the parameters used at time t and t+1 are identical (which is not the case when target parameters are to be used). Defaults to False.

• 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.

• 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".

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

forward(tensordict: TensorDictBase = None, *, params: tensordict.base.TensorDictBase | None = None, target_params: tensordict.base.TensorDictBase | None = None) → TensorDictBase

Computes the TD(0) 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 TDEstimate. 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 = TDEstimate(
...     gamma=0.98,
...     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, "terminated": terminated, "reward": reward}}, [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 = TDEstimate(
...     gamma=0.98,
...     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: Optional[TensorDictBase] = None, next_value: Optional[Tensor] = 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.