Papers › CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement Learning

CAMMARL: Conformal Action Modeling in Multi Agent Reinforcement Learning

19 Jun 2023arXiv:2306.11128archive 2025-07-28

Nikunj Gupta, Somjit Nath, Samira Ebrahimi Kahou

Before taking actions in an environment with more than one intelligent agent, an autonomous agent may benefit from reasoning about the other agents and utilizing a notion of a guarantee or confidence about the behavior of the system. In this article, we propose a novel multi-agent reinforcement learning (MARL) algorithm CAMMARL, which involves modeling the actions of other agents in different situations in the form of confident sets, i.e., sets containing their true actions with a high probability. We then use these estimates to inform an agent's decision-making. For estimating such sets, we use the concept of conformal predictions, by means of which, we not only obtain an estimate of the most probable outcome but get to quantify the operable uncertainty as well. For instance, we can predict a set that provably covers the true predictions with high probabilities (e.g., 95%). Through several experiments in two fully cooperative multi-agent tasks, we show that CAMMARL elevates the capabilities of an autonomous agent in MARL by modeling conformal prediction sets over the behavior of other agents in the environment and utilizing such estimates to enhance its policy learning.

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Conformal PredictionDecision MakingMulti-agent Reinforcement LearningReinforcement Learningreinforcement-learning

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