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Typo docstring (#1132)

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bordeauxred 2024-05-01 08:59:00 +02:00 committed by GitHub
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tianshou/policy/base.py
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@ -556,7 +556,7 @@ class BasePolicy(nn.Module, Generic[TTrainingStats], ABC):
advantage + value, which is exactly equivalent to using :math:`TD(\lambda)`
for estimating returns.
Setting v_s_ and v_s to None (or all zeros) and gae_lambda to 1.0 calculates the
Setting `v_s_` and `v_s` to None (or all zeros) and `gae_lambda` to 1.0 calculates the
discounted return-to-go/ Monte-Carlo return.
:param batch: a data batch which contains several episodes of data in
@ -564,12 +564,12 @@ class BasePolicy(nn.Module, Generic[TTrainingStats], ABC):
should be marked by done flag, unfinished (or collecting) episodes will be
recognized by buffer.unfinished_index().
:param buffer: the corresponding replay buffer.
:param numpy.ndarray indices: tell batch's location in buffer, batch is equal
:param indices: tells the batch's location in buffer, batch is equal
to buffer[indices].
:param np.ndarray v_s_: the value function of all next states :math:`V(s')`.
:param v_s_: the value function of all next states :math:`V(s')`.
If None, it will be set to an array of 0.
:param v_s: the value function of all current states :math:`V(s)`. If None,
it is set based upon v_s_ rolled by 1.
it is set based upon `v_s_` rolled by 1.
:param gamma: the discount factor, should be in [0, 1].
:param gae_lambda: the parameter for Generalized Advantage Estimation,
should be in [0, 1].