How can agents communicate effectively despite varying visibility?
TACTIC: Task-Agnostic Contrastive pre-Training for Inter-Agent Communication
This paper introduces TACTIC, a novel approach for improving communication and coordination in multi-agent reinforcement learning (MARL) systems, especially when agents have limited or varying "sight ranges" (observability). TACTIC uses a task-agnostic contrastive pre-training method to teach agents how to communicate effectively, regardless of their specific task or how much of the environment they can directly see. This pre-training allows a single, adaptable model to handle diverse visibility conditions, eliminating the need for retraining with every change in observability.
For LLM-based multi-agent systems, TACTIC’s focus on robust communication under limited or dynamic observability is particularly relevant. LLMs, while powerful, can struggle with context integration when dealing with partial information. TACTIC's contrastive learning approach could help LLMs within a multi-agent system learn to share and integrate information more effectively, even with limited individual context, enabling better coordination and decision-making. The task-agnostic nature of TACTIC is also beneficial, as it means the communication model could be reused across various applications without requiring retraining.