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

On-Policy MC Control – Exploring starts

The algorithm for on-policy MC control by exploring the starts method is given as follows:

Let total_return(s, a) be the sum of the return of a state-action pair across several episodes and N(s, a) be the number of times a state-action pair is visited across several episodes. Initialize total_return(s, a) and N(s, a) for all state-action pairs to zero and initialize a random policy .
For M number of iterations:
1. Select the initial state s₀ and initial action a₀ randomly such that all state-action pairs have a probability greater than 0
2. Generate an episode from the selected initial state s₀ and action a₀ using policy
3. Store all the rewards obtained in the episode in the list called rewards
4. For each step t in the episode:
  If (s_t, a_t) is occurring for the first time in the episode:
  1. Compute the return of a state-action pair, R(s_t, a_t) = sum(rewards[t:...