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 Gym

With OpenAI Gym, we can simulate a variety of environments and develop, evaluate, and compare RL algorithms. Let's now understand how to use Gym.

Basic simulations

Let's see how to simulate a basic cart pole environment:

  1. First, let's import the library:
import gym
  1. The next step is to create a simulation instance using the make function:
env = gym.make('CartPole-v0')
  1. Then we should initialize the environment using the reset method:
  1. Then we can loop for some time steps and render the environment at each step:
for _ in range(1000):

The complete code is as follows:

import gym 
env = gym.make(