Multi-Agent Game Abstraction via Graph Attention Neural Network (1911.10715v1)

Abstract: 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.

• The paper proposes a two-stage Graph Attention Neural Network that prunes irrelevant interactions and assigns weights to key agent relationships.
• It integrates G2ANet with reinforcement learning architectures, namely GA-Comm and GA-AC, to achieve superior convergence rates and performance.
• Empirical results in Traffic Junction and Predator-Prey scenarios demonstrate the method's scalability and efficiency in complex multi-agent environments.

Multi-Agent Game Abstraction via Graph Attention Neural Network

In large-scale multi-agent systems, the complexity introduced by numerous agents and intricate interaction webs poses significant challenges for effective policy learning. The paper "Multi-Agent Game Abstraction via Graph Attention Neural Network" presents a novel approach to simplifying this process through an innovative game abstraction mechanism leveraging a Graph Attention Neural Network (G2ANet). This framework is pivotal in identifying relevant interactions among agents, thereby refining the cognitive load and potential communication overhead during the learning phase.

Key Contributions

1. Two-Stage Attention Mechanism: At the core of the proposed methodology lies the G2ANet, which employs a two-stage attention network. This mechanism integrates hard and soft attention strategies, effectively pruning irrelevant interactions while assigning appropriate weights to meaningful ones. Hard-attention generates a binary output to signify presence or absence of effective relationships, while soft-attention refines these relationships by assessing their significance using continuous weights.
2. Integration with Reinforcement Learning Architectures: The paper advances two innovative reinforcement learning paradigms, GA-Comm and GA-AC, which embed the G2ANet’s game abstraction capability into communication-nurtured and actor-critic models respectively. GA-Comm incorporates the complete agent communication networks, while GA-AC integrates game abstraction within the critic’s evaluation of agents' policies.
3. Empirical Evaluation: Rigorous experimentation is conducted within the addictive frameworks of Traffic Junction and Predator-Prey scenarios. Comparative results against state-of-the-art multi-agent settings indicate that both GA-Comm and GA-AC algorithms demonstrate superior convergence rates and enhanced asymptotic performance.

Implications and Future Perspectives

The proposed methodologies open several avenues in the ongoing research of multi-agent reinforcement learning (MARL), especially within environments characterized by sparse yet significant agent interactions. By dynamically discerning and adapting to key interaction patterns, systems developed via the G2ANet are not only computationally efficient but also scalable.

One of the significant implications is the method’s utility in real-world applications such as autonomous driving networks, where parallel entities must collaborate reliably without prior exhaustive knowledge of interaction dynamics. Given this capability, future research could focus on augmenting the transferability of learned policies across disparate environments—a step toward achieving robust and adaptable intelligence in autonomous systems.

Furthermore, the integration of more sophisticated graph-based representations and learning frameworks, such as incorporating spatio-temporal reasoning or enhancing scalability through hierarchical organizations, remains a viable path for exploration. These enhancements would entail the incorporation of higher-order attention attributes alongside the existing G2ANet framework, thereby potentially addressing even more complex behavioral dynamics present in natural and artificial ecologies.

By contextualizing the immediate and long-term relevance of learned interactions, this paper enhances the fidelity of decision-making processes among agents, thus bolstering the overall objectives of efficiency, coordination, and theoretical underpinnings in multi-agent systems.