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Adversarial attacks against reinforcement learning agents

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Reinforcement learning agents have progressed by leaps and bounds over the last few years. We now have self-driving cars from Tesla, competitive game playing agents from OpenAI and Deepmind and many more.

While our knowledge about such technologies has improved, so too has our knowledge about the inherent weaknesses in such systems.

In this presentation, I will be discussing how reinforcement learning agents work, how adversarial attacks take advantage of weaknesses to fool these agents, and some of the defences we have against such attacks.

This talk is part of the Churchill CompSci Talks series.

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