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arXiv · 2206.11812

Formalizing the Problem of Side Effect Regularization

Alexander Matt Turner·Aseem Saxena·Prasad Tadepalli
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Abstract

AI objectives are often hard to specify properly. Some approaches tackle this problem by regularizing the AI's side effects: Agents must weigh off "how much of a mess they make" with an imperfectly specified proxy objective. We propose a formal criterion for side effect regularization via the assistance game framework. In these games, the agent solves a partially observable Markov decision process (POMDP) representing its uncertainty about the objective function it should optimize. We consider the setting where the true objective is revealed to the agent at a later time step. We show that this POMDP is solved by trading off the proxy reward with the agent's ability to achieve a range of future tasks. We empirically demonstrate the reasonableness of our problem formalization via ground-truth evaluation in two gridworld environments.

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Alexander Matt Turner, Aseem Saxena, Prasad Tadepalli. 2022-06-23. Formalizing the Problem of Side Effect Regularization. https://arxiv.org/abs/2206.11812

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