Summary

This chapter commenced with configuring the working environment, followed by an examination of the core concepts of reinforcement learning, accompanied by practical examples. Subsequently, we delved into the FrozenLake environment, employing dynamic programming techniques such as value iteration and policy iteration to tackle it effectively. Monte Carlo learning was introduced for value estimation and control in the Blackjack environment. Finally, we implemented the Q-learning algorithm to address the same problem, providing a comprehensive overview of reinforcement learning techniques.