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Agents often operate using imperfect models of the environment that ignore certain aspects of the real world. Reasoning with such models may lead to negative side effects (NSE) when satisfying the primary objective of the available model, which are inherently difficult to identify at design time. We examine how various forms of feedback can be used to learn a penalty function associated with NSE during execution. We formulate the problem of mitigating the impact of NSE as a multi-objective Markov decision process with lexicographic reward preferences and slack. Empirical evaluation of our approach on three domains shows that the proposed framework can successfully mitigate NSE.

Mitigating the Negative Side Effects of Reasoning with Imperfect Models: A Multi-Objective Approach