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Sampling of VPG should be over D*T #361

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Sampling of VPG should be over D*T #361

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4 changes: 2 additions & 2 deletions docs/spinningup/rl_intro3.rst
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Expand Up @@ -91,7 +91,7 @@ This is an expectation, which means that we can estimate it with a sample mean.

.. math::

\hat{g} = \frac{1}{|\mathcal{D}|} \sum_{\tau \in \mathcal{D}} \sum_{t=0}^{T} \nabla_{\theta} \log \pi_{\theta}(a_t |s_t) R(\tau),
\hat{g} = \frac{1}{|\mathcal{D*T}|} \sum_{\tau \in \mathcal{D}} \sum_{t=0}^{T} \nabla_{\theta} \log \pi_{\theta}(a_t |s_t) R(\tau),

where :math:`|\mathcal{D}|` is the number of trajectories in :math:`\mathcal{D}` (here, :math:`N`).

Expand Down Expand Up @@ -474,4 +474,4 @@ In this chapter, we described the basic theory of policy gradient methods and co
.. _`advantage of an action`: ../spinningup/rl_intro.html#advantage-functions
.. _`this page`: ../spinningup/extra_pg_proof2.html
.. _`Generalized Advantage Estimation`: https://arxiv.org/abs/1506.02438
.. _`Vanilla Policy Gradient`: ../algorithms/vpg.html
.. _`Vanilla Policy Gradient`: ../algorithms/vpg.html