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

Twin delayed DDPG

Now, we will look into another interesting actor-critic algorithm, known as TD3. TD3 is an improvement (and basically a successor) to the DDPG algorithm we just covered.

In the previous section, we learned how DDPG uses a deterministic policy to work on the continuous action space. DDPG has several advantages and has been successfully used in a variety of continuous action space environments.

We understood that DDPG is an actor-critic method where an actor is a policy network and it finds the optimal policy, while the critic evaluates the policy produced by the actor by estimating the Q function using a DQN.

One of the problems with DDPG is that the critic overestimates the target Q value. This overestimation causes several issues. We learned that the policy is improved based on the Q value given by the critic, but when the Q value has an approximation error, it causes stability issues to our policy and the policy may converge to local optima.

Thus...