Book Image

Hands-On Reinforcement Learning with Python

By : Sudharsan Ravichandiran
Book Image

Hands-On Reinforcement Learning with Python

By: Sudharsan Ravichandiran

Overview of this book

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. Hands-On Reinforcement learning with Python will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep reinforcement learning algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many more of the recent advancements in reinforcement learning. By the end of the book, you will have all the knowledge and experience needed to implement reinforcement learning and deep reinforcement learning in your projects, and you will be all set to enter the world of artificial intelligence.
Table of Contents (16 chapters)

Summary

In this chapter, we have learned about the MAB problem and how it can be applied to different applications. We understood several methods to solve an explore-exploit dilemma. First, we looked at the epsilon-greedy policy, where we explored with the probability epsilon, and carried out exploration with the probability 1-epsilon. We looked at the UCB algorithm, where we picked up the best action with the maximum upper bound value, followed by the TS algorithm, where we picked up the best action via beta distribution.

In the upcoming chapters, we will learn about deep learning and how deep learning is used to solve RL problems.