From ca29ddfbf9dfeedd7acb290e8e386b4dd8669f11 Mon Sep 17 00:00:00 2001
From: Lutz von der Burchard <61054407+lutzvdb@users.noreply.github.com>
Date: Mon, 15 Jan 2024 09:45:50 +0100
Subject: [PATCH] Update mid-way-recap.mdx

Compare issue 451 (https://github.com/huggingface/deep-rl-class/issues/451)
---
 units/en/unit2/mid-way-recap.mdx | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/units/en/unit2/mid-way-recap.mdx b/units/en/unit2/mid-way-recap.mdx
index c5da116..b644040 100644
--- a/units/en/unit2/mid-way-recap.mdx
+++ b/units/en/unit2/mid-way-recap.mdx
@@ -8,7 +8,7 @@ We have two types of value-based functions:
 - Action-value function: outputs the expected return if **the agent starts in a given state, takes a given action at that state** and then acts accordingly to the policy forever after.
 - In value-based methods, rather than learning the policy, **we define the policy by hand** and we learn a value function. If we have an optimal value function, we **will have an optimal policy.**
 
-There are two types of methods to learn a policy for a value function:
+There are two types of methods to update the value function:
 
 - With *the Monte Carlo method*, we update the value function from a complete episode, and so we **use the actual discounted return of this episode.**
 - With *the TD Learning method,* we update the value function from a step, replacing the unknown \\(G_t\\) with **an estimated return called the TD target.**