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1
Intro
2
DRL in Games
3
Modeling an RL Problem
4
Solving an RL Problem
5
DRL-powered Games
6
Training an DRL Bot
7
Perturbation-based Attacks
8
Adversarial Agent Attack
9
Our attack
10
Quantitively Evaluation • Comparison of winning rates.
11
Demo Examples
12
A Potential Defense
Description:
Explore the vulnerabilities of AI-powered game bots in this 40-minute Black Hat conference talk. Delve into the world of deep reinforcement learning (DRL) algorithms and their application in complex games like StarCraft. Learn about modeling and solving reinforcement learning problems, training DRL bots, and various attack methods including perturbation-based and adversarial agent attacks. Examine quantitative evaluations of winning rates, view demo examples, and discuss potential defense strategies against these exploits. Gain insights from experts Xinyu Xing, Wenbo Guo, Xian Wu, and Jimmy Su as they reveal the blind spots of AI in gaming and demonstrate how to exploit them.

Ruling StarCraft Game Spitefully - Exploiting the Blind Spot of AI-Powered Game Bots

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