Summary
In this chapter, we introduced a different class of machine learning problems that focus on automating decisions by agents that interact with an environment. We covered the key features required to define an RL problem and various solution methods.
We saw how to frame and analyze an RL problem as a finite Markov decision problem, as well as how to compute a solution using value and policy iteration. We then moved on to more realistic situations, where the transition probabilities and rewards are unknown to the agent, and saw how Q-learning builds on the key recursive relationship defined by the Bellman optimality equation in the MDP case. We saw how to solve RL problems using Python for simple MDPs and more complex environments with Q-learning.
We then expanded our scope to continuous states and applied the Deep Q-learning algorithm to the more complex Lunar Lander environment. Finally, we designed a simple trading environment using the OpenAI Gym platform, and also...
