Artificial Intelligence IV - Reinforcement Learning in Java
- 4.1
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Brief Introduction
All you need to know about Markov Decision processes, value- and policy-iteation as well as about Q learning approachDescription
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
Requirements
- Requirements
- Basics AI knowledge: neural networks in the main
Holczer Balazs
4.1 Raiting
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Artificial Intelligence IV - Reinforcement Learning in Java
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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....
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- 4.3
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