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

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

Chapter 5 – Understanding Temporal Difference Learning

Unlike the Monte Carlo method, the Temporal Difference (TD) learning method makes use of bootstrapping so that we don't have to wait until the end of the episode to compute the value of a state.
The TD learning algorithm takes the benefits of both the dynamic programming and the Monte Carlo methods into account. That is, just like the dynamic programming method, we perform bootstrapping so that we don't have to wait till the end of an episode to compute the state value or Q value and just like the Monte Carlo method, it is a model-free method, and so it does not require the model dynamics of the environment to compute the state value or Q value.
The TD error can be defined as the difference between the target value and predicted value.
The TD learning update rule is given as .
In a TD prediction task, given a policy, we estimate the value function using the given policy. So, we...