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)

Temporal Difference Learning

In the previous chapter, Chapter 4, Gaming with Monte Carlo Methods, we learned about the interesting Monte Carlo method, which is used for solving the Markov Decision Process (MDP) when the model dynamics of the environment are not known in advance, unlike dynamic programming. We looked at the Monte Carlo prediction method, which is used for predicting value functions and control methods for further optimizing value functions. But there are some pitfalls with the Monte Carlo method. It is applied only for episodic tasks. If an episode is very long, then we have to wait a long time for computing value functions. So, we will use another interesting algorithm called temporal-difference (TD) learning, which is a model-free learning algorithm: it doesn't require the model dynamics to be known in advance and it can be applied for non-episodic tasks...