Hands-On Reinforcement Learning with Java [Video]

Hands-On Reinforcement Learning with Java

Hands-On Reinforcement Learning with Java [Video]

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 23m | 306 MB
eLearning | Skill level: All Levels

Hands-On Reinforcement Learning with Java [Video]: Solve real-world problems by employing reinforcement learning techniques with Java

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.

  • 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

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.

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