Artificial Intelligence IV - Reinforcement Learning in Java

Artificial Intelligence IV - Reinforcement Learning in Java

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

All you need to know about Markov Decision processes, value- and policy-iteation as well as about Q learning approach

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What you will learn

• Understand reinforcement learning
• Understand Markov Decision Processes
• Understand value- and policy-iteration

Rating: 4.65

Level: All Levels

Duration: 3 hours

Instructor: Holczer Balazs