From 23e06a36722a1355f61f3316ccb252053eb16f60 Mon Sep 17 00:00:00 2001
From: Patrick Helm <91552435+PatrickHelm@users.noreply.github.com>
Date: Tue, 21 Nov 2023 11:20:03 +0100
Subject: [PATCH] Fix entropy equation

---
 docs/algorithms/sac.rst | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/algorithms/sac.rst b/docs/algorithms/sac.rst
index 6df7ff501..ff677f824 100644
--- a/docs/algorithms/sac.rst
+++ b/docs/algorithms/sac.rst
@@ -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:
 
@@ -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
\ No newline at end of file
+.. _`Yarats and Kostrikov repo`: https://github.com/denisyarats/pytorch_sac