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

Inverse reinforcement learning

Inverse Reinforcement Learning (IRL) is one of the most exciting fields of reinforcement learning. In reinforcement learning, our goal is to learn the optimal policy. That is, our goal is to find the optimal policy that gives the maximum return (sum of rewards of the trajectory). In order to find the optimal policy, first, we should know the reward function. A reward function tells us what reward we obtain by performing an action a in the state s. Once we have the reward function, we can train our agent to learn the optimal policy that gives the maximum reward. But the problem is that designing the reward function is not that easy for complex tasks.

Consider designing the reward function for tasks such as an agent learning to walk, self-driving cars, and so on. In these cases, designing the reward function is not that handy and involves assigning rewards to a variety of agent behaviors. For instance, consider designing the reward function for an...