Book Image

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On

By: Maxim Lapan

Overview of this book

Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.
Table of Contents (23 chapters)
Deep Reinforcement Learning Hands-On
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Chapter 10. The Actor-Critic Method

In Chapter 9, Policy Gradients – An Alternative, we started to investigate an alternative to the familiar value-based methods family, called policy-based. In particular, we focused on the method called REINFORCE and its modification that uses a discounted reward to obtain the gradient of the policy (which gives us the direction to improve the policy). Both methods worked well for a small CartPole problem, but for a more complicated Pong environment, the convergence dynamic was painfully slow.

In this chapter, we'll discuss one more extension to the vanilla Policy Gradient (PG) method, which magically improves the stability and convergence speed of the new method. Despite the modification being only minor, the new method has its own name, Actor-Critic, and it's one of the most powerful methods in deep Reinforcement Learning (RL).