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
Contributors
Preface
Other Books You May Enjoy
Index

Trust Region Policy Optimization


TRPO was proposed in 2015 by the Berkeley researchers in the paper by John Schulman et al called Trust Region Policy Optimization (arXiv:1502.05477). This paper was a step towards improving the stability and consistency of the stochastic policy gradient optimization and has shown good results on various control tasks.

Unfortunately, the paper and the method are quite math-heavy, so it can be hard to understand the details of the method. The same could be said about the implementation, which uses the conjugate gradients method to efficiently solve the constrained optimization problem.

As the first step, the TRPO method defines the discounted visitation frequencies of the state: . In this equation, equals to the sampled probability of state s to be met at position i of the sampled trajectories. Then, TRPO defines the optimization objective as where is the expected discount reward of the policy and defines the deterministic policy.

To address the issue with...