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 12 – Learning DDPG, TD3, and SAC

DDPG consists of an actor and critic. The actor is a policy network and uses the policy gradient method for learning the optimal policy. The critic is a DQN and it evaluates the action produced by the actor.
The critic is basically a DQN. The goal of the critic is to evaluate the action produced by the actor network. The critic evaluates the action produced by the actor using the Q value computed by the DQN.
The key features of TD3 includes clipped double Q learning, delayed policy updates, and target policy smoothing.
Instead of using one critic network, we use two main critic networks for computing the Q value and we use two target critic networks for computing the target value. We compute two target Q values using two target critic networks and use the minimum value out of these two while computing the loss. This helps in preventing the overestimation of the target Q value.
The DDPG method...