PIMAEX: Multi-Agent Exploration through Peer Incentivization

• URL: http://arxiv.org/abs/2501.01266v1
• Date: Thu, 02 Jan 2025 14:06:52 GMT
• Title: PIMAEX: Multi-Agent Exploration through Peer Incentivization
• Authors: Michael Kölle, Johannes Tochtermann, Julian Schönberger, Gerhard Stenzel, Philipp Altmann, Claudia Linnhoff-Popien,
• Abstract summary: This work proposes a peer-incentivized reward function inspired by previous research on intrinsic curiosity and influence-based rewards.<n>The textitPIMAEX reward aims to improve exploration in the multi-agent setting by encouraging agents to exert influence over each other.
• Score: 4.860484063961962
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While exploration in single-agent reinforcement learning has been studied extensively in recent years, considerably less work has focused on its counterpart in multi-agent reinforcement learning. To address this issue, this work proposes a peer-incentivized reward function inspired by previous research on intrinsic curiosity and influence-based rewards. The \textit{PIMAEX} reward, short for Peer-Incentivized Multi-Agent Exploration, aims to improve exploration in the multi-agent setting by encouraging agents to exert influence over each other to increase the likelihood of encountering novel states. We evaluate the \textit{PIMAEX} reward in conjunction with \textit{PIMAEX-Communication}, a multi-agent training algorithm that employs a communication channel for agents to influence one another. The evaluation is conducted in the \textit{Consume/Explore} environment, a partially observable environment with deceptive rewards, specifically designed to challenge the exploration vs.\ exploitation dilemma and the credit-assignment problem. The results empirically demonstrate that agents using the \textit{PIMAEX} reward with \textit{PIMAEX-Communication} outperform those that do not.

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