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 the MC method applicable to all tasks?

We learned that Monte Carlo is a model-free method, and so it doesn't require the model dynamics of the environment to compute the value and Q function in order to find the optimal policy. The Monte Carlo method computes the value function and Q function by just taking the average return of the state and the average return of the state-action pair, respectively.

But one issue with the Monte Carlo method is that it is applicable only to episodic tasks. We learned that in the Monte Carlo method, we compute the value of the state by taking the average return of the state and the return is the sum of rewards of the episode. But when there is no episode, that is, if our task is a continuous task (non-episodic task), then we cannot apply the Monte Carlo method.

Okay, how do we compute the value of the state where we have a continuous task and also where we don't know the model dynamics of the environment? Here is where...