How can attention improve multi-agent task allocation?
Attention-Augmented Inverse Reinforcement Learning with Graph Convolutions for Multi-Agent Task Allocation
April 8, 2025
https://arxiv.org/pdf/2504.05045This paper proposes a novel approach to Multi-Agent Task Allocation (MATA) using Inverse Reinforcement Learning (IRL) to improve how autonomous agents learn to collaborate on tasks. Instead of manually specifying rewards for completing tasks, the system learns what is valuable by observing expert demonstrations. It leverages attention mechanisms (MHSA and graph attention networks) to better understand relationships between agents, tasks, and their environment, improving coordination and efficiency.
Key points for LLM-based multi-agent systems:
- Reward learning from demonstrations: IRL offers a promising way to define rewards for complex multi-agent scenarios where manual specification is difficult, opening potential for LLMs to act as "experts" by providing demonstrations or generating training data.
- Attention mechanisms for improved coordination: The use of attention mechanisms like MHSA and GAT allows the agents to focus on important information about their surroundings and other agents, analogous to attention in LLMs. This can inspire similar architectures for improved information processing and communication in LLM-based multi-agent systems.
- Potential for global state awareness: The graph attention network integrates global state information, enhancing agent coordination. This concept is relevant to LLM-based systems where agents might benefit from a shared understanding of the overall situation.
- Adaptability and scalability: The proposed method showed improved performance across different task and agent densities, which are crucial considerations for complex LLM-based multi-agent applications.