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

MAML in Reinforcement Learning

The algorithm for MAML in the reinforcement learning setting is given as follows:

Say we have a model f parameterized by a parameter and we have a distribution over tasks p(T). First, we randomly initialize the model parameter .
Sample a batch of tasks T_i from a distribution of tasks, that is, T_i~ p(T).
For each task T_i:
1. Sample k trajectories using and prepare the training dataset:
2. Train the model on the training dataset and compute the loss
3. Minimize the loss using gradient descent and get the optimal parameter as
4. Sample k trajectories using and prepare the test dataset:
Now, we minimize the loss on the test dataset . Parameterize the model f with the optimal parameter calculated in the previous step and compute the loss . Calculate the gradients of the loss and update our randomly initialized parameter using our test (meta-training) dataset:
Repeat...