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)

Environment wrapper functions

The credit for the code used in this chapter goes to Giacomo Spigler's GitHub repository ( Throughout this chapter, the code is explained at each and every line. For a complete structured code, check the above GitHub repository.

First, we import all the necessary libraries:

import numpy as np
import tensorflow as tf
import gym
from gym.spaces import Box
from scipy.misc import imresize
import random
import cv2
import time
import logging
import os
import sys

We define the EnvWrapper class and define some of the environment wrapper functions:

class EnvWrapper:

We define the __init__ method and initialize variables:

   def __init__(self, env_name, debug=False):

Initialize the gym environment:

        self.env = gym.make(env_name)

Get the action_space:

        self.action_space = self.env.action_space...