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1
Introduction
2
What is Reinforcement Learning
3
What is Value Function
4
Problem Statement
5
Actions
6
Environments
7
Courier task
8
Architecture
9
Training
10
Act of Creating
11
Actor Critic
12
Results
13
Multisite Experiments
14
Abolition Analysis
15
Demo
Description:
Explore the fascinating world of reinforcement learning applied to urban navigation in this 30-minute lecture from the University of Central Florida. Delve into key concepts such as value functions and actor-critic models, and discover how they can be applied to solve complex courier tasks in unmapped city environments. Examine the problem statement, actions, and environments involved in this challenging scenario. Learn about the architecture and training process behind the solution, including multisite experiments and abolition analysis. Gain insights into the act of creating AI systems for real-world applications, and witness the results through an engaging demo.

Learning to Navigate in Cities Without a Map

University of Central Florida
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