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 10 – Policy Gradient Method

In the value-based method, we extract the optimal policy from the optimal Q function (Q values).
It is difficult to compute optimal policy using the value-based method when our action space is continuous. So, we use the policy-based method. In the policy-based method, we compute the optimal policy without the Q function.
In the policy gradient method, we select actions based on the action probability distribution given by the network and if we win the episode, that is, if we get a high return, then we assign high probabilities to all the actions of the episode, else we assign low probabilities to all the actions of the episode.
The policy gradient is computed as .
Reward-to-go is basically the return of the trajectory starting from the state s_t. It is computed as .
The policy gradient with the baseline function is a policy gradient method that uses the baseline function to reduce the variance...