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

Exploring activation functions

An activation function, also known as a transfer function, plays a vital role in neural networks. It is used to introduce non-linearity in neural networks. As we learned before, we apply the activation function to the input, which is multiplied by weights and added to the bias, that is, f(z), where z = (input * weights) + bias and f(.) is the activation function.

If we do not apply the activation function, then a neuron simply resembles the linear regression. The aim of the activation function is to introduce a non-linear transformation to learn the complex underlying patterns in the data.

Now let's look at some of the interesting commonly used activation functions.

The sigmoid function

The sigmoid function is one of the most commonly used activation functions. It scales the value between 0 and 1. The sigmoid function can be defined as follows: