This is the code repository for Hands-On Reinforcement Learning with Java [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.
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.
- 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
For successful completion of this course, students will require the computer systems with at least the following:
• IntelliJ IDEA
• Java JDK 8 or later
For an optimal experience with hands-on labs and other practical activities, we recommend the following configuration:
• Processor: I7 2.8
• Memory: 16GB
• Hard Disk Space: 200MB
• Video Card: 256MB Video Memory