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

Meta reinforcement learning

In order to understand how meta reinforcement learning works, first let's understand meta learning.

Meta learning is one of the most promising and trending research areas in the field of artificial intelligence. It is believed to be a stepping stone for attaining Artificial General Intelligence (AGI). What is meta learning? And why do we need meta learning? To answer these questions, let's revisit how deep learning works.

We know that in deep learning, we train a deep neural network to perform a task. But the problem with deep neural networks is that we need to have a large training dataset to train our network, as it will fail to learn when we have only a few data points.

Let's say we trained a deep learning model to perform task A. Suppose we have a new task B, which is closely related to task A. Although task B is closely related to task A, we can't use the model we trained for task A to perform task B. We need to train...