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

Imagination augmented agents

Are you a fan of chess? If I asked you to play chess, how would you play it? Before moving any chess piece on the chessboard, you might imagine the consequences of moving a chess piece and move the chess piece that you think would help you to win the game. So, basically, before taking any action, we imagine the consequence and, if it is favorable, we proceed with that action, else we refrain from performing that action.

Similarly, Imagination Augmented Agents (I2As) are augmented with imagination. Before taking any action in an environment, the agent imagines the consequences of taking the action and if they think the action will provide a good reward, they will perform the action. The I2A takes advantage of both model-based and model-free learning. Figure 17.8 shows the architecture of I2As: