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 3 – The Bellman Equation and Dynamic Programming

The Bellman equation states that the value of a state can be obtained as a sum of the immediate reward and the discounted value of the next state. Similar to the Bellman equation of the value function, the Bellman equation of the Q function states that the Q value of a state-action pair can be obtained as a sum of the immediate reward and the discounted Q value of the next state-action pair.
The Bellman expectation equation gives the Bellman value and Q functions whereas the Bellman optimality equation gives the optimal Bellman value and Q functions.
The value function can be derived from the Q function as .
The Q function can be derived from the value function as .
In the value iteration method, we perform the following steps:
1. Compute the optimal value function by taking maximum over Q function, that is,
2. Extract the optimal policy from the computed...