Book Image

Hands-On Reinforcement Learning with Python

By : Sudharsan Ravichandiran
Book Image

Hands-On Reinforcement Learning with Python

By: Sudharsan Ravichandiran

Overview of this book

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
Table of Contents (16 chapters)

Summary

In this chapter, we learned how the A3C network works. In A3C, Asynchronous implies multiple agents working independently by interacting with multiple copies of the environment, Advantage implies the advantage function, which is the difference between the Q function and the value function, and Actor Critic refers to the Actor Critic network, where the actor network is responsible for generating a policy and the critic network evaluates the policy generated by the actor network. We have seen how A3C works, and saw how to solve a mountain car problem using the algorithm.

In the next chapter, Chapter 11, Policy Gradients and Optimization, we will see policy gradient methods that directly optimize the policy without requiring the Q function.