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
Other Books You May Enjoy

Proximal Policy Optimization

Historically, this method came from the OpenAI team and was proposed long after TRPO (which is from 2015), but PPO is much simpler than TRPO, so we'll start from it. The paper in which it was proposed is by John Schulman et al and called Proximal Policy Optimization Algorithms, published in 2017 (arXiv:1707.06347).

The core improvement over the classical Asynchronous Advantage Actor-Critic (A3C) method is to change the expression used to estimate the PG. Instead of the gradient of logarithm probability of the action taken, the PPO method uses a different objective: the ratio between the new and the old policy scaled by the advantages.

In math form, the old A3C objective could be written as . The new objective proposed by the PPO is . The reason behind changing the objective is the same as for the cross-entropy method from Chapter 4, The Cross-Entropy Method: importance sampling. However, if we just start to blindly maximize this value, it may lead to a very large...