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

Is DP applicable to all environments?

In dynamic programming, that is, in the value and policy iteration methods, we try to find the optimal policy.

Value iteration: In the value iteration method, we compute the optimal value function by taking the maximum over the Q function (Q values) iteratively:

Where . After finding the optimal value function, we extract the optimal policy from it.

Policy iteration: In the policy iteration method, we compute the optimal value function using the policy iteratively:

We will start off with the random policy and compute the value function. Once we have found the optimal value function, then the policy that is used to create the optimal value function will be the optimal policy.

If you look at the preceding two equations, in order to find the optimal policy, we compute the value function and Q function. But to compute the value and the Q function, we need to know the transition probability of the environment, and...