You're reading from Deep Reinforcement Learning with Python Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow

Product type Paperback

Published in Sep 2020

Publisher Packt

ISBN-13 9781839210686

Length 760 pages

Edition 2nd Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Sudharsan Ravichandiran

View More author details

Table of Contents (22) Chapters

Preface

1. Fundamentals of Reinforcement Learning

2. A Guide to the Gym Toolkit FREE CHAPTER

3. The Bellman Equation and Dynamic Programming

4. Monte Carlo Methods

5. Understanding Temporal Difference Learning

6. Case Study – The MAB Problem

7. Deep Learning Foundations

8. A Primer on TensorFlow

9. Deep Q Network and Its Variants

10. Policy Gradient Method

11. Actor-Critic Methods – A2C and A3C

12. Learning DDPG, TD3, and SAC

13. TRPO, PPO, and ACKTR Methods

14. Distributional Reinforcement Learning

15. Imitation Learning and Inverse RL

16. Deep Reinforcement Learning with Stable Baselines

17. Reinforcement Learning Frontiers

18. Other Books You May Enjoy

19. Index

Appendix 1 – Reinforcement Learning Algorithms

1. Appendix 2 – Assessments

Actor-critic using Kronecker-factored trust region

ACKTR, as the name suggests, is the actor-critic algorithm based on the Kronecker factorization and trust region.

We know that the actor-critic architecture consists of the actor and critic networks, where the role of the actor is to produce a policy and the role of the critic is to evaluate the policy produced by the actor network. We learned that in the actor network (policy network), we compute gradients and update the parameter of the actor network using gradient ascent:

Instead of updating our actor network parameter using the preceding update rule, we can also update it by computing the natural gradients as:

Where F is called the Fisher information matrix. Thus, the natural gradient is just the product of the inverse of the Fisher matrix and standard gradient: