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

Supervised imitation learning

In the imitation learning setting, our goal is to mimic the expert. Say, we want to train our agent to drive a car. Instead of training the agent from scratch by having them interact with the environment, we can train them with expert demonstrations. Okay, what are expert demonstrations? An expert demonstrations are a set of trajectories consisting of state-action pairs where each action is performed by the expert.

We can train an agent to mimic the actions performed by the expert in various respective states. Thus, we can view expert demonstrations as training data used to train our agent. The fundamental idea of imitation learning is to imitate (learn) the behavior of an expert.

One of the simplest and most naive ways to perform imitation learning is to treat the imitation learning task as a supervised learning task. First, we collect a set of expert demonstrations, and then we train a classifier to perform the same action performed by the...