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Keep shoppingArtificial Intelligence IV - Reinforcement Learning in Java
2,148 students
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
Created byHolczer Balazs
Last updated 1/2025
English
What you'll learn
- Understand reinforcement learning
- Understand Markov Decision Processes
- Understand value- and policy-iteration
- Understand Q-learning approach and it's applications
Explore related topics
Course content
10 sections • 50 lectures • 5h 13m total length
Requirements
- Basics AI knowledge: neural networks in the main
Description
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
Who this course is for:
- Anyone who wants to understand artificial intelligence and reinforcement learning!