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)

OpenAI Universe

OpenAI Universe provides a wide range of realistic gaming environments. It is an extension to OpenAI Gym. It provides the ability to train and evaluate agents on a wide range of simple to real-time complex environments. It has unlimited access to many gaming environments.

Building a video game bot

Let's learn how to build a video game bot which plays a car racing game. Our objective is that the car has to move forward without getting stuck on any obstacles or hitting other cars.

First, we import the necessary libraries:

import gym
import universe # register universe environment
import random

Then we simulate our car racing environment using the make function:

env = gym.make('flashgames.NeonRace-v0...