Hands-On Reinforcement Learning with Java | Packt

Hands-On Reinforcement Learning with Java | Packt
English | Size: 281.60 MB
Genre: eLearning

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



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