MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English (US) | Size: 777.64 MB | Duration: 3h 1m
All you need to know about Markov Decision processes, value- and policy-iteation as well as about Q learning approach
What you’ll learn
Understand reinforcement learning
Understand Markov Decision Processes
Understand value- and policy-iteration
Understand Q-learning approach and it’s applications
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-iterationQ-learning fundamentalspathfinding algorithms with Q-learningQ-learning with neural networks
Who this course is for
Anyone who wants to understand artificial intelligence and reinforcement learning!
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