Out-of-distribution detection

Illusory Attacks: Detectability Matters in Adversarial Attacks on Sequential Decision-Makers

We introduce illusory attacks, a novel form of adversarial attacks on reinforcement learning agents that is of bounded statistical detectability.

Tim Franzmeyer, Stephen Marcus McAleer, Joao F. Henriques, Jakob Nicolaus Foerster, Philip Torr, Adel Bibi, Christian Schroeder de Witt

Illusory Attacks: Detectability Matters in Adversarial Attacks on Sequential Decision-Makers

Rethinking Out-of-Distribution Detection for Reinforcement Learning: Advancing Methods for Evaluation and Detection

We propose DEXTER, a novel out-of-distribution dynamics detection method achieving state-of-the-art in this setting.

Linas Nasvytis, Kai Sandbrink, Jakob Foerster, Tim Franzmeyer, Christian Schroeder de Witt

Rethinking Out-of-Distribution Detection for Reinforcement Learning: Advancing Methods for Evaluation and Detection