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)

Training an agent to play Doom

Doom is a very popular first-person shooter game. The goal of the game is to kill monsters. Doom is another example of a partially observable MDP as the agent's (player) view is limited to 90 degrees. The agent has no idea about the rest of the environment. Now, we will see how can we use DRQN to train our agent to play Doom.

Instead of OpenAI Gym, we will use the ViZDoom package to simulate the Doom environment to train our agent. To learn more about the ViZDoom package, check out its official website at We can install ViZDoom simply by using the following command:

pip install vizdoom

ViZDoom provides a lot of Doom scenarios and those scenarios can be found in the package folder vizdoom/scenarios.

Basic Doom game...