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

Understanding computational graphs and sessions

As we have learned, every computation in TensorFlow is represented by a computational graph. They consist of several nodes and edges, where nodes are mathematical operations, such as addition and multiplication, and edges are tensors. Computational graphs are very efficient at optimizing resources and promote distributed computing.

A computational graph consists of several TensorFlow operations, arranged in a graph of nodes.

A computational graph helps us to understand the network architecture when we work on building a really complex neural network. For instance, let's consider a simple layer, h = Relu(WX + b). Its computational graph would be represented as follows:

Figure 8.1: Computational graph

There are two types of dependency in the computational graph, called direct and indirect dependency. Say we have node b, the input of which is dependent on the output of node a; this type of dependency is called...