Tabitha Edith Lee

Postdoctorat - UdeM

Superviseur⋅e principal⋅e

Glen Berseth

Co-supervisor

Yoshua Bengio

Sujets de recherche

Apprentissage de représentations

Apprentissage par renforcement

Apprentissage profond

Causalité

Robotique

Publications

AI Agent Safety is a Reinforcement Learning Problem

Reginald McLean

Tabitha Edith Lee

Montaser Mohammedalamen

Kevin Roice

Glen Berseth

Patrick M. Pilarski

Marlos C. Machado

Alyssa Lefaivre Škopac

Benjamin Rosman

With the rapid advancement and deployment of Agentic AI, our scientific understanding of capabilities and limitations has not kept pace, lea… (voir plus)ding to cases where AI agents cause harm. We argue that many of these safety limitations are not novel problems. Instead, the safety challenges currently facing AI agents can be seen as instances of problems the reinforcement learning (RL) community has studied rigorously for decades. The core of this argument concerns the problem formulation of AI agents. AI agents are designed to solve sequential decision-making problems: problems with long-term objectives in which actions have delayed consequences. To model these types of problem, the problem is set up the problem such that the agent receives observations, feedback on its progress, and then takes actions. This is precisely the formulation of the RL problem. In this paper, we formalize the problem equivalence, which we then leverage to argue that \textbf{AI Agent safety is a reinforcement learning problem: the failure modes currently observed in deployed AI agents are structural instances of problems RL has formalized for decades, and the RL safety literature provides principled tools to diagnose and address them.}. We conclude with a call for deliberate collaboration between the RL and AI agent research communities: AI agent researchers gain access to principled frameworks, while RL researchers gain a class of real-world problems that could expose fundamental gaps in current RL benchmarks and theory.

2026-05-22

AIWILD @ International Conference on Machine Learning (publié)

openreview.net