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 15. Trust Regions – TRPO, PPO, and ACKTR

In this chapter, we'll take a look at the approaches used to improve the stability of the stochastic policy gradient method. Some attempts have been made to make the policy improvement more stable and we'll focus on three methods: Proximal Policy Optimization (PPO), Trust Region Policy Optimization (TRPO) and Actor-Critic (A2C) using Kronecker-Factored Trust Region (ACKTR).

To compare them to the A2C baseline, we'll use several environments from the roboschool library created by OpenAI.