1. Müller, R., Illium, S., Phan, T., Haider, T., and Linnhoff-Popien, C. 2022. Towards Anomaly Detection in Reinforcement Learning. Proceedings of the 21st International Conference on Autonomous Agents and Multiagent Systems, 1799–1803.

This work investigates anomaly detection (AD) within reinforcement learning (RL), highlighting its importance in safety-critical applications due to the complexity of sequential decision-making in RL. The study criticizes the simplicity of current AD research scenarios in RL, connecting AD to lifelong RL and generalization, discussing their interrelations and potential mutual benefits. It identifies non-stationarity as a crucial area for future AD research in RL, proposing a formal approach through the block contextual Markov decision process and outlining practical requirements for future studies. [Müller et al. 2022]

Formal Definition