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 11 – Actor-Critic Methods – A2C and A3C

The actor-critic method is one of the most popular algorithms in deep RL. Several modern deep RL algorithms are designed based on the actor-critic method. The actor-critic method lies at the intersection of value-based and policy-based methods. That is, it takes advantage of both value-based and policy-based methods.
In the actor-critic method, the actor computes the optimal policy and the critic evaluates the policy computed by the actor network by estimating the value function.
In the policy gradient method with baseline, first, we generate complete episodes (trajectories), and then we update the parameter of the network, whereas, in the actor-critic method, we update the network parameter at every step of the episode.
In the actor network, we compute the gradient as .
In advantage actor-critic (A2C), we compute the policy gradient with the advantage function and the advantage function...