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)

Agent environment interface

Agents are the software agents that perform actions, At, at a time, t, to move from one state, St, to another state St+1. Based on actions, agents receive a numerical reward, R, from the environment. Ultimately, RL is all about finding the optimal actions that will increase the numerical reward:

Let us understand the concept of RL with a maze game:

The objective of a maze is to reach the destination without getting stuck on the obstacles. Here's the workflow:

  • The agent is the one who travels through the maze, which is our software program/ RL algorithm
  • The environment is the maze
  • The state is the position in a maze that the agent currently resides in
  • An agent performs an action by moving from one state to another
  • An agent receives a positive reward when its action doesn't get stuck on any obstacle and receives a negative reward when its action gets stuck on obstacles so it cannot reach the destination
  • The goal is to clear the maze and reach the destination