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 4 – Monte Carlo Methods

In the Monte Carlo method, we approximate the value of a state by taking the average return of a state across N episodes instead of taking the expected return.
To compute the value function using the dynamic programming method, we need to know the model dynamics, and when we don't know the model dynamics, we use model-free methods. The Monte Carlo method is a model-free method meaning that it doesn't require the model dynamics (transition probability) to compute the value function.
In a prediction task, we evaluate the given policy by predicting the value function or Q function, which helps us to understand the expected return an agent would get if it used the given policy. However, in a control task, our goal is to find the optimal policy and are not given any policy as input, so we start by initializing a random policy and try to find the optimal policy iteratively.
In the MC prediction method, the value...