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Conference proceeding
Encoding Goals as Graphs: AAMAS '26: Proceedings of the 25th International Conference on Autonomous Agents and Multiagent Systems
Proc. of the 25th International Conference on Autonomous Agents and Multiagent Systems, pp.3513-3515
ACM Conferences
AAMAS 2026: Autonomous Agents and Multiagent Systems, 25th (Paphos Cyprus, 05/25/2026–05/29/2026)
05/25/2026
Abstract
Many cooperative multi-agent tasks are naturally defined by graph-structured objectives, where agents must collectively achieve a desired relational configuration or satisfy a set of constraints. However, current goal-conditioned multi-agent reinforcement learning (MARL) methods rarely leverage such symbolic structure to guide learning. To address this challenge, we propose Graph Embeddings for Multi-Agent Coordination (GEMA), which augments any cooperative learner with a State-Graph Encoder (SGE). The SGE is pre-trained contrastively to embed state and goal graphs in a shared metric space. At run time, each agent constructs the state graph, queries the SGE, and computes a similarity score to the goal embedding. This similarity serves as an intrinsic reward, providing dense feedback on task progress, and is also incorporated into each agent's observation. Experiments on cooperative navigation, load balancing, and the StarCraft Multi-Agent Challenge (v2) show that GEMA accelerates convergence and improves team returns.
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Details
- Title
- Encoding Goals as Graphs
- Publication Details
- Proc. of the 25th International Conference on Autonomous Agents and Multiagent Systems, pp.3513-3515
- Resource Type
- Conference proceeding
- Conference
- AAMAS 2026: Autonomous Agents and Multiagent Systems, 25th (Paphos Cyprus, 05/25/2026–05/29/2026)
- Publisher
- International Foundation for Autonomous Agents and Multiagent Systems; RichlandSC
- Series
- ACM Conferences
- Copyright
- © 2026 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org).
- Identifiers
- 99381803057006600
- Academic Unit
- Institute for Human and Machine Cognition; Intelligent Systems and Robotics; Hal Marcus College of Science and Engineering
- Language
- English