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 17 – Reinforcement Learning Frontiers

Meta learning produces a versatile AI model that can learn to perform various tasks without having to train them from scratch. We train our meta-learning model on various related tasks with a few data points, so for a new but related task, it can make use of the learning obtained from the previous tasks and we don't have to train it from scratch.
Model-Agnostic Meta Learning (MAML) is one of the most popularly used meta-learning algorithms and it has created a major breakthrough in meta-learning research. The basic idea of MAML is to find a better initial model parameter so that with good initial parameters, the model can learn quickly on new tasks with fewer gradient steps.
In the outer loop of MAML, we update the model parameter as and it is known as a meta objective.
The meta training set basically acts as a training set in the outer loop and is used to update the model parameter in the outer...