Book Image

Keras Reinforcement Learning Projects

By : Giuseppe Ciaburro
Book Image

Keras Reinforcement Learning Projects

By: Giuseppe Ciaburro

Overview of this book

Reinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks. Keras Reinforcement Learning Projects installs human-level performance into your applications using algorithms and techniques of reinforcement learning, coupled with Keras, a faster experimental library. The book begins with getting you up and running with the concepts of reinforcement learning using Keras. You’ll learn how to simulate a random walk using Markov chains and select the best portfolio using dynamic programming (DP) and Python. You’ll also explore projects such as forecasting stock prices using Monte Carlo methods, delivering vehicle routing application using Temporal Distance (TD) learning algorithms, and balancing a Rotating Mechanical System using Markov decision processes. Once you’ve understood the basics, you’ll move on to Modeling of a Segway, running a robot control system using deep reinforcement learning, and building a handwritten digit recognition model in Python using an image dataset. Finally, you’ll excel in playing the board game Go with the help of Q-Learning and reinforcement learning algorithms. By the end of this book, you’ll not only have developed hands-on training on concepts, algorithms, and techniques of reinforcement learning but also be all set to explore the world of AI.
Table of Contents (13 chapters)

Temporal difference learning

TD learning algorithms are based on reducing the differences between estimates made by the agent at different times. Q-learning, which we will discuss in the following section, is a TD algorithm, but it is based on the difference between states in immediately adjacent instants. TD is more generic and may consider moments and states further away.

TD is a combination of the ideas of the MC method and DP, both of which can be summarized as follows:

  • MC methods allow the solving of reinforcement learning problems based on the average of the obtained results
  • DP represents a set of algorithms that can be used to calculate an optimal policy given a perfect model of the environment in the form of a Markov Decision Process (MDP)

The following can be said of TD methods:

  • They inherit from MC methods the idea of learning directly from experience accumulated...