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

Summary

We started this chapter by understanding what meta learning is. We learned that with meta learning, we train our model on various related tasks with a few data points, such that for a new related task, our model can make use of the learning obtained from the previous tasks.

Next, we learned about a popular meta-learning algorithm called MAML. In MAML, we sample a batch of tasks and for each task T_i in the batch, we minimize the loss using gradient descent and get the optimal parameter . Then, we update our randomly initialized model parameter by calculating the gradients for each of the new tasks T_i with the model parameterized as .

Moving on, we learned about HRL, where we decompose large problems into small subproblems in a hierarchy. We also looked into the different methods used in HRL, such as state-space decomposition, state abstraction, and temporal abstraction. Next, we got an overview of MAXQ value function decomposition, where we decompose the...