Udemy – Artificial Intelligence IV Reinforcement Learning in Java
English | Tutorial | Size: 777.56 MB
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
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