Inverse Attention Agents for Multi-Agent Systems
Qian Long, Ruoyan Li, Minglu Zhao, Tao Gao, Demetri Terzopoulos
TL;DR
This paper introduces Inverse Attention Agents that utilize a Theory of Mind-inspired attention mechanism to adapt dynamically in multi-agent environments, significantly improving performance and human-like cooperation.
Contribution
The paper presents a novel inverse attention network that infers other agents' attentional states, enhancing adaptability and cooperation in multi-agent systems.
Findings
Inverse attention network accurately infers other agents' attention.
Improved agent performance in cooperation and competition tasks.
Agents better emulate human behaviors in experiments.
Abstract
A major challenge for Multi-Agent Systems is enabling agents to adapt dynamically to diverse environments in which opponents and teammates may continually change. Agents trained using conventional methods tend to excel only within the confines of their training cohorts; their performance drops significantly when confronting unfamiliar agents. To address this shortcoming, we introduce Inverse Attention Agents that adopt concepts from the Theory of Mind (ToM) implemented algorithmically using an attention mechanism trained in an end-to-end manner. Crucial to determining the final actions of these agents, the weights in their attention model explicitly represent attention to different goals. We furthermore propose an inverse attention network that deduces the ToM of agents based on observations and prior actions. The network infers the attentional states of other agents, thereby refining…
Peer Reviews
Decision·ICLR 2025 Poster
- The paper proposes a novel approach to attention mechanism in the multi-agent system setting. - The results are tested both with unseen artificial agents and with human agents' interaction with the proposed agent.
- it is not clear to me and was not discussed in the text how the agent observation can be decomposed into a combination of goals. - The authors collect the attention inference dataset but do not provide an analysis of how different realizations of such attention weights collections may influence the resulting inverse-attention agent performance (e.g. dataset collected with models with different training hyperparameters, random seeds, attention sizes). - The authors provide no ablation study.
The proposed method approaches the problem of inferring intentions of agents by allowing each agent to access the observation of other agents and reason about what their intentions are. The approach offers a simple yet effective tool for multi-agent systems especially for heavily correlated environments, for example in dense environments. Altering agent behavior through explicit reasoning over other agents intentions is of great interest to the multi-agent community.
As part of the scalability analysis, a maximum number of 8 agents have been tested. Due to the concatenation of each agents attention weight, I would like an analysis on the limitations of the scalability, particularly for the accuracy of IW predictions and the complexity of processing each pairwise agent inference separately as number of agents grow to 20~50 agents.
1. This work uses the gradient field (GF) representations to represent the goals of the agent within specific environments, which is an interesting idea. 2. This work conducts a human study to demonstrate the effectiveness of the proposed method.
1. The proposed inverse attention network requires the observation of agent $j$ when inferring the attention weights of agent $j$. Therefore, the proposed method only works in fully observable environments and will face problems in partially observable environments if the observations of other agents are unavailable. 2. The proposed method assumes the observations of an agent can be decomposed into a combination of $N$ goals within the environment. This assumption holds for the tested environme
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Taxonomy
TopicsAdvanced Algorithms and Applications
MethodsSoftmax · Attention Is All You Need