June 2018
Intermediate to advanced
318 pages
9h 24m
English
Content preview from Hands-On Reinforcement Learning with Python
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The policy function
We have learned about the policy function in Chapter 1, Introduction to Reinforcement Learning, which maps the states to actions. It is denoted by π.
The policy function can be represented as 
, indicating mapping from states to actions. So, basically, a policy function says what action to perform in each state. Our ultimate goal lies in finding the optimal policy which specifies the correct action to perform in each state, which maximizes the reward.
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