Packt | Hands-On Reinforcement Learning with Java


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Oct 21, 2018

By: Tomasz Lelek
Released: August 23, 2019 (New Release!)
Torrent Contains: 31 Files, 7 Folders
Course Source:

Solve real-world problems by employing reinforcement learning techniques with Java

Video Details

Course Length 1 hour 23 minutes

Table of Contents

• Deep Dive into Reinforcement Learning with DL4J ? RL4J
• Solving Cartpole with Markov Decision Processes (MDPs)
• Using Project Malmo ? Reinforcement Learning Leveraging Dynamic Programming
• Creating Decision Process for Stock Prediction with Rewards Using Q-Learning
• Leveraging Monte Carlo Tree Searches and Temporal Difference (TD) in RL


• Leverage ND4J with RL4J for reinforcement learning
• Use Markov Decision Processes to solve the cart-pole problem
• Use QLConfiguration to configure your reinforcement learning algorithms
• Leverage dynamic programming to solve the cliff walking problem
• Use Q-learning for stock prediction
• Solve problems with the Asynchronous Advantage Actor-Critic technique
• Use RL4J with external libraries to speed up your reinforcement learning models


There are problems in data science and the ML world that cannot be solved with supervised or unsupervised learning. When the standard ML engineer's toolkit is not enough, there is a new approach you can learn and use: reinforcement learning.

This course focuses on key reinforcement learning techniques and algorithms in the Java ecosystem. Each section covers RL concepts and solves real-world problems. You will learn to solve challenging problems such as creating bots, decision-making, random cliff walking, and more. Then you will also cover deep reinforcement learning and learn how you can add a deep neural network with DeepLearning4J in your RL algorithm.

By the end of this course, you'll be ready to tackle reinforcement learning problems and leverage the most powerful Java DL libraries to create your reinforcement learning algorithms.

The code bundle for this course is available at


• Use reinforcement learning with DL4J and RL4J to solve problems with high accuracy
• Learn how to use the ND4J and RL4J libraries with external libraries such as Malmo to abstract complex algorithms and make them easy to use
• Implement q-learning, Markov Decision Processes (MDPs), dynamic programming, and other reinforcement techniques to solve real-world problems.