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

Prediction and control tasks

In reinforcement learning, we perform two important tasks, and they are:

The prediction task
The control task

Prediction task

In the prediction task, a policy is given as an input and we try to predict the value function or Q function using the given policy. But what is the use of doing this? Our goal is to evaluate the given policy. That is, we need to determine whether the given policy is good or bad. How can we determine that? If the agent obtains a good return using the given policy then we can say that our policy is good. Thus, to evaluate the given policy, we need to understand what the return the agent would obtain if it uses the given policy. To obtain the return, we predict the value function or Q function using the given policy.

That is, we learned that the value function or value of a state denotes the expected return an agent would obtain starting from that state following some policy . Thus, by predicting...