In this chapter, you've learned various reinforcement learning techniques, like Markov decision process, Bellman equations, dynamic programming, Monte Carlo methods, Temporal Difference learning, including both on-policy (SARSA) and off-policy (Q-learning), with Python examples to understand its implementation in a practical way. You also learned how Q-learning is being used in many practical applications nowadays, as this method learns from trial and error by interacting with environments.
Next, we looked at some other practical applications for reinforcement learning with machine learning, and deep learning utilized to solve state-of-the-art problems.
Finally, Further reading has been provided for you if you would like to pursue reinforcement learning full-time. We wish you all the best!