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

The MAB problem

The MAB problem is one of the classic problems in reinforcement learning. A MAB is a slot machine where we pull the arm (lever) and get a payout (reward) based on some probability distribution. A single slot machine is called a one-armed bandit and when there are multiple slot machines it is called a MAB or k-armed bandit, where k denotes the number of slot machines.

Figure 6.1 shows a 3-armed bandit:

Figure 6.1: 3-armed bandit slot machines

Slot machines are one of the most popular games in the casino, where we pull the arm and get a reward. If we get 0 reward then we lose the game, and if we get +1 reward then we win the game. There can be several slot machines, and each slot machine is referred to as an arm. For instance, slot machine 1 is referred to as arm 1, slot machine 2 is referred to as arm 2, and so on. Thus, whenever we say arm n, it actually means that we are referring to slot machine n.

Each arm has its own probability distribution...