Artificial Intelligence IV – Reinforcement Learning in Java

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Price: 199.99$

This course is about Reinforcement Learning. The first step is to talk about the mathematical background: we can use a Markov Decision Processasa model forreinforcement 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 Processesvalue-iteration and policy-iteration Q-learning fundamentalspathfinding algorithms with Q-learning Q-learning with neural networks