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Avoiding Negative Side Effects by Considering Others
Parand Alizadeh Alamdari · Toryn Klassen · Rodrigo Toro Icarte · Sheila McIlraith

Recent work in AI safety has highlighted that in sequential decision making, objectives are often underspecified or incomplete. This potentially allows the AI agent to make undesirable changes to the world while achieving its given objective. A number of recent papers have proposed avoiding such negative side effects by giving an auxiliary reward to the agent for preserving its own ability to complete tasks or gain reward. We argue that effects on others need to be explicitly considered and provide a formulation that generalizes prior work. We experimentally investigate our approach with RL agents in gridworlds.

Author Information

Parand Alizadeh Alamdari (University of Toronto)
Toryn Klassen (University of Toronto)
Rodrigo Toro Icarte (University of Toronto and Vector Institute)

I am a Ph.D. student in the knowledge representation group at the University of Toronto. I am also a member of the Canadian Artificial Intelligence Association and the Vector Institute. My supervisor is Sheila McIlraith. I did my undergrad in Computer Engineering and MSc in Computer Science at Pontificia Universidad Catolica de Chile (PUC). My master's degree was co-supervised by Alvaro Soto and Jorge Baier. While I was at PUC, I taught the undergraduate course "Introduction to Computer Programming Languages."

Sheila McIlraith (University of Toronto and Vector Institute)

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