Artificial Intelligence IV Reinforcement Learning in Java

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Description

This course is about Reinforcement Learning. The first step is to talk about the mathematical background: we can use a Markov Decision Process as a model for reinforcement learning. We can solve the problem 3 ways: value-iteration, policy-iteration and Q-learning. Q-learning is a model free approach so it is state-of-the-art approach. It learns the optimal policy by interacting with the environment. So these are the topics:

 
  •  Markov Decision Processes
  •  value-iteration and policy-iteration
  • Q-learning fundamentals
  • pathfinding algorithms with Q-learning
  • Q-learning with neural networks

Who this course is for:

  • Anyone who wants to understand artificial intelligence and reinforcement learning!

Requirements

  • Basics AI knowledge: neural networks in the main
Artificial Intelligence IV Reinforcement Learning in Java
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Course details
Duration 7.5 total hours
Lectures 1
Level Beginner
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Contact

  • USA, Callifornia 20, First Avenue, Callifornia
  • Tel.: +1 212 458 300 32
  • Fax: +1 212 375 24 14
Artificial Intelligence IV Reinforcement Learning in Java
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