Merge pull request #466 from lutzvdb/patch-2

Update mid-way-recap.mdx

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.**