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 9 – Deep Q Network and Its Variants

When the environment consists of a large number of states and actions, it will be very expensive to compute the Q value of all possible state-action pairs in an exhaustive fashion. So, we use a deep Q network for approximating the Q function.
We use a buffer called the replay buffer to collect the agent's experience and based on this experience, we train our network. The replay buffer is usually implemented as a queue structure (first in, first out) rather than a list. So, if the buffer is full and the new experience comes in, we remove the old experience and add the new experience into the buffer.
When the target and predicted values depend on the same parameter , it will cause instability in the mean squared error and the network will learn poorly. It also causes a lot of divergence during training. So, we use a target network.
Unlike with DQNs, in double DQNs, we compute the target value using two...