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Fix entropy equation in SAC documentation #403

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Fix entropy equation in SAC documentation #403

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6 changes: 3 additions & 3 deletions docs/algorithms/sac.rst
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Expand Up @@ -35,11 +35,11 @@ Entropy-Regularized Reinforcement Learning

Entropy is a quantity which, roughly speaking, says how random a random variable is. If a coin is weighted so that it almost always comes up heads, it has low entropy; if it's evenly weighted and has a half chance of either outcome, it has high entropy.

Let :math:`x` be a random variable with probability mass or density function :math:`P`. The entropy :math:`H` of :math:`x` is computed from its distribution :math:`P` according to
Let :math:`x` be a random variable with probability mass or density function :math:`p`. The entropy :math:`H` of :math:`x` is computed from its distribution :math:`P` according to

.. math::

H(P) = \underE{x \sim P}{-\log P(x)}.
H(P) = \underE{x \sim P}{-\log p(x)}.

In entropy-regularized reinforcement learning, the agent gets a bonus reward at each time step proportional to the entropy of the policy at that timestep. This changes `the RL problem`_ to:

Expand Down Expand Up @@ -318,4 +318,4 @@ Other Public Implementations

.. _`SAC release repo`: https://github.com/haarnoja/sac
.. _`Softlearning repo`: https://github.com/rail-berkeley/softlearning
.. _`Yarats and Kostrikov repo`: https://github.com/denisyarats/pytorch_sac
.. _`Yarats and Kostrikov repo`: https://github.com/denisyarats/pytorch_sac