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)

The Markov Decision Process and Dynamic Programming

The Markov Decision Process (MDP) provides a mathematical framework for solving the reinforcement learning (RL) problem. Almost all RL problems can be modeled as MDP. MDP is widely used for solving various optimization problems. In this chapter, we will understand what MDP is and how can we use it to solve RL problems. We will also learn about dynamic programming, which is a technique for solving complex problems in an efficient way.

In this chapter, you will learn about the following topics:

  • The Markov chain and Markov process
  • The Markov Decision Process
  • Rewards and returns
  • The Bellman equation
  • Solving a Bellman equation using dynamic programming
  • Solving a frozen lake problem using value and policy iteration