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

Policy gradient intuition

Policy gradient is one of the most popular algorithms in deep reinforcement learning. As we have learned, policy gradient is a policy-based method by which we can find the optimal policy without computing the Q function. It finds the optimal policy by directly parameterizing the policy using some parameter .

The policy gradient method uses a stochastic policy. We have learned that with a stochastic policy, we select an action based on the probability distribution over the action space. Say we have a stochastic policy , then it gives the probability of taking an action a given the state s. It can be denoted by . In the policy gradient method, we use a parameterized policy, so we can denote our policy as , where indicates that our policy is parameterized.

Wait! What do we mean when we say a parameterized policy? What is it exactly? Remember with DQN, we learned that we parameterize our Q function to compute the Q value? We can do the same here, except...