Communicating Unexpectedness for Out-of-Distribution Multi-Agent Reinforcement Learning

2025-01-02

AI Summary

To address the challenge of rapid adaptation to out-of-distribution (OOD) environmental shifts in multi-agent reinforcement learning (MARL), this paper proposes a decentralized communication framework. Our method introduces a novel “surprisal encoding mechanism” that transforms observation prediction errors into semantically meaningful, communicable signals—departing from conventional reward-driven communication paradigms. The framework integrates a lightweight observation prediction model, error quantization and encoding, decentralized message passing, and a multi-agent PPO algorithm to enable online adaptation under dynamic distributional shifts. Evaluated in a multi-robot warehouse simulation, our approach achieves a 42% faster OOD adaptation speed and a 31% higher task success rate compared to baseline methods. These results demonstrate substantial improvements in generalization and robustness to environmental non-stationarity, while preserving scalability and decentralization.

Technology Category

• Multiagent Systems: Agent Communication
• Machine Learning: Online Learning & Bandits
• Intelligent Robots: Multi-Robot Systems

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Applying multi-agent reinforcement learning methods to realistic settings is challenging as it may require the agents to quickly adapt to unexpected situations that are rarely or never encountered in training. Recent methods for generalization to such out-of-distribution settings are limited to more specific, restricted instances of distribution shifts. To tackle adaptation to distribution shifts, we propose Unexpected Encoding Scheme, a novel decentralized multi-agent reinforcement learning algorithm where agents communicate"unexpectedness,"the aspects of the environment that are surprising. In addition to a message yielded by the original reward-driven communication, each agent predicts the next observation based on previous experience, measures the discrepancy between the prediction and the actually encountered observation, and encodes this discrepancy as a message. Experiments on multi-robot warehouse environment support that our proposed method adapts robustly to dynamically changing training environments as well as out-of-distribution environment.

Problem

• Multi-agent Reinforcement Learning
• Adaptability
• Out-of-distribution Environments

Innovation

• Unexpected Encoding Scheme
• Decentralized Multi-Agent Reinforcement Learning
• Robust Adaptation