Artificial Intelligence IV - Reinforcement Learning in Java

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Created by Holczer Balazs | Video: 1280x720 | Audio: AAC 48KHz 2ch | Duration: 03:02 H/M | Lec: 39 | 349 MB | Language: English | Sub: English [Auto-generated]

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-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!
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