# Multi-Agent Game Abstraction via Graph Attention Neural Network

arXiv: https://arxiv.org/abs/1911.10715 · HTML: https://mlsanity.com/paper/1911.10715/

## Key facts

- Cited by 82 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2019.

## Built on by (most cited first, 30 of 82)

- [A Survey of Multi-Agent Deep Reinforcement Learning with Communication](https://mlsanity.com/paper/2203.08975/) (2022)
- [A General Survey on Attention Mechanisms in Deep Learning](https://mlsanity.com/paper/2203.14263/) (2022)
- [Towards a Standardised Performance Evaluation Protocol for Cooperative MARL](https://mlsanity.com/paper/2209.10485/) (2022)
- [Learning Multi-Agent Communication from Graph Modeling Perspective](https://mlsanity.com/paper/2405.08550/) (2024)
- [Scalable Multi-Agent Reinforcement Learning through Intelligent Information Aggregation](https://mlsanity.com/paper/2211.02127/) (2022)
- [Multi-agent Reinforcement Learning: A Comprehensive Survey](https://mlsanity.com/paper/2312.10256/) (2023)
- [Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey](https://mlsanity.com/paper/2408.09675/) (2024)
- [Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications](https://mlsanity.com/paper/2206.07922/) (2022)
- [Scalable Multi-Agent Model-Based Reinforcement Learning](https://mlsanity.com/paper/2205.15023/) (2022)
- [Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunities](https://mlsanity.com/paper/2506.18019/) (2025)
- [Group-Aware Coordination Graph for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2404.10976/) (2024)
- [Distributed Reinforcement Learning for Robot Teams: A Review](https://mlsanity.com/paper/2204.03516/) (2022)
- [Generative Emergent Communication: Large Language Model is a Collective World Model](https://mlsanity.com/paper/2501.00226/) (2024)
- [QGNN: Value Function Factorisation with Graph Neural Networks](https://mlsanity.com/paper/2205.13005/) (2022)
- [Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication](https://mlsanity.com/paper/2105.10716/) (2021)
- [Iterated Reasoning with Mutual Information in Cooperative and Byzantine Decentralized Teaming](https://mlsanity.com/paper/2201.08484/) (2022)
- [Context-aware Communication for Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2312.15600/) (2023)
- [Decentralized Transformers with Centralized Aggregation are Sample-Efficient Multi-Agent World Models](https://mlsanity.com/paper/2406.15836/) (2024)
- [Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2201.04612/) (2022)
- [ESP: Exploiting Symmetry Prior for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2307.16186/) (2023)
- [Efficient Multi-agent Reinforcement Learning by Planning](https://mlsanity.com/paper/2405.11778/) (2024)
- [LERO: LLM-driven Evolutionary framework with Hybrid Rewards and Enhanced Observation for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2503.21807/) (2025)
- [Joint Attention for Multi-Agent Coordination and Social Learning](https://mlsanity.com/paper/2104.07750/) (2021)
- [Interaction Pattern Disentangling for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2207.03902/) (2022)
- [Inferring Latent Temporal Sparse Coordination Graph for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2403.19253/) (2024)
- [FCMNet: Full Communication Memory Net for Team-Level Cooperation in Multi-Agent Systems](https://mlsanity.com/paper/2201.11994/) (2022)
- [Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control](https://mlsanity.com/paper/2310.02435/) (2023)
- [BMG-Q: Localized Bipartite Match Graph Attention Q-Learning for Ride-Pooling Order Dispatch](https://mlsanity.com/paper/2501.13448/) (2025)
- [Deep Meta Coordination Graphs for Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2502.04028/) (2025)
- [Exponential Topology-enabled Scalable Communication in Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2502.19717/) (2025)

## Abstract (arXiv)

In large-scale multi-agent systems, the large number of agents and complex game relationship cause great difficulty for policy learning. Therefore, simplifying the learning process is an important research issue. In many multi-agent systems, the interactions between agents often happen locally, which means that agents neither need to coordinate with all other agents nor need to coordinate with others all the time. Traditional methods attempt to use pre-defined rules to capture the interaction relationship between agents. However, the methods cannot be directly used in a large-scale environment due to the difficulty of transforming the complex interactions between agents into rules. In this paper, we model the relationship between agents by a complete graph and propose a novel game abstraction mechanism based on two-stage attention network (G2ANet), which can indicate whether there is an interaction between two agents and the importance of the interaction. We integrate this detection mechanism into graph neural network-based multi-agent reinforcement learning for conducting game abstraction and propose two novel learning algorithms GA-Comm and GA-AC. We conduct experiments in Traffic Junction and Predator-Prey. The results indicate that the proposed methods can simplify the learning process and meanwhile get better asymptotic performance compared with state-of-the-art algorithms.