Year: 2026
Type: article
Abstract: In cooperative multi-agent reinforcement learning (MARL), communication can address the challenges of partial observability and environmental non-stationarity by conveying environmental features and decision intents, respectively. However, existing methods either focus on only one type of information—failing to tackle both challenges simultaneously—or conflate these signals, causing agents to confuse environmental context with decision intents. This paper introduces Disentangling Environment and Decision messages for Multi-Agent Communication (DEDMAC), a framework that explicitly separates these two information types into two distinct message streams and processes them independently. Specifically, environment messages are integrated into long-term memory to resolve partial observability, while decision messages provide instantaneous intent signals to mitigate non-stationarity and facilitate coordination. To prevent semantic confusion between the two message streams, we employ mutual information constraints to ensure semantic disentanglement. Furthermore, we design a mechanism that leverages global information to correct intent biases in decision messages resulting from limited local perspectives during generation. Evaluations across complex multi-agent benchmarks demonstrate that DEDMAC significantly outperforms state-of-the-art communication-based methods. These findings indicate that the explicit separation and specialized processing of environment and decision semantics are critical for achieving optimal performance in dynamic, collaborative multi-agent systems.
Source: Information
DOI: 10.3390/info17040332
Authors: Yihan Liang, Jinlong Li
Institution: University of Science and Technology of China
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,930
OpenAlex ID: W7147286945