Artificial Intelligence IV - Reinforcement Learning in Java

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

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