In this chapter, we learned about RL and how it's different from supervised and unsupervised learning. The emphasis of this chapter was on DRL, where deep neural networks are used to approximate the policy function or the value function or even both. This chapter introduced OpenAI gym, a library that provides a large number of environments to train RL agents. We learned about the value-based methods such as Q-learning and used it to train an agent to pick up and drop passengers off in a taxi. We also used a DQN to train an agent to play a Atari game . This chapter then moved on to policy-based methods, specifically policy gradients. We covered the intuition behind policy gradients and used the algorithm to train an RL agent to play Pong.
In the next chapter, we'll explore generative models and learn the secrets behind generative adversarial networks...
