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Keras Reinforcement Learning Projects

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  • Practice the Markov decision process in prediction and betting evaluations
  • Implement Monte Carlo methods to forecast environment behaviors
  • Explore TD learning algorithms to manage warehouse operations
  • Construct a Deep Q-Network using Python and Keras to control robot movements
  • Apply reinforcement concepts to build a handwritten digit recognition model using an image dataset
  • Address a game theory problem using Q-Learning and OpenAI Gym

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.


  • Build projects across robotics, gaming, and finance fields, putting reinforcement learning (RL) into action
  • Get to grips with Keras and practice on real-world unstructured datasets
  • Uncover advanced deep learning algorithms such as Monte Carlo, Markov Decision, and Q-learning
Page Count 288
Course Length 8 hours 38 minutes
ISBN 9781789342093
Date Of Publication 28 Sep 2018


Giuseppe Ciaburro

Giuseppe Ciaburro holds a PhD in environmental technical physics, along with two master's degrees. His research was focused on machine learning applications in the study of urban sound environments. He works at the Built Environment Control Laboratory at the Università degli Studi della Campania Luigi Vanvitelli, Italy. He has over 15 years' professional experience in programming (Python, R, and MATLAB), first in the field of combustion, and then in acoustics and noise control. He has several publications to his credit.