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

Hierarchical reinforcement learning

The problem with reinforcement learning is that it cannot scale well with a large number of state spaces and actions, which ultimately leads to the problem called curse of dimensionality. Hierarchical reinforcement learning (HRL) is proposed to solve the curse of dimensionality, where we decompose large problems into small subproblems in a hierarchy. Let's suppose the goal of our agent is to reach home from school. Now, our goal is split into a set of subgoals, such as going out of the school gate, booking a cab, and so on.

There are different methods used in HRL, such as state-space decomposition, state abstraction, and temporal abstraction. In state-space decomposition, we decompose the state space into different subspaces and try to solve the problem in a smaller subspace. Breaking down the state space also allows faster exploration, as the agent does not want to explore the entire state space. In state abstraction, the agent ignores...