Learning Individually Inferred Communication for Multi-Agent Cooperation

TL;DR

This paper introduces Individually Inferred Communication (I2C), a novel multi-agent communication model that learns to infer communication needs, reducing overhead and enhancing cooperation performance through causal inference and neural networks.

Contribution

The paper proposes I2C, a new approach enabling agents to learn communication priors via causal inference, improving multi-agent cooperation efficiency and effectiveness.

Findings

Reduces communication overhead in multi-agent systems.

Improves cooperative performance across various scenarios.

Learns communication needs through causal inference.

Abstract

Communication lays the foundation for human cooperation. It is also crucial for multi-agent cooperation. However, existing work focuses on broadcast communication, which is not only impractical but also leads to information redundancy that could even impair the learning process. To tackle these difficulties, we propose Individually Inferred Communication (I2C), a simple yet effective model to enable agents to learn a prior for agent-agent communication. The prior knowledge is learned via causal inference and realized by a feed-forward neural network that maps the agent's local observation to a belief about who to communicate with. The influence of one agent on another is inferred via the joint action-value function in multi-agent reinforcement learning and quantified to label the necessity of agent-agent communication. Furthermore, the agent policy is regularized to better exploit communicated messages. Empirically, we show that I2C can not only reduce communication overhead but also improve the performance in a variety of multi-agent cooperative scenarios, comparing to existing methods. The code is available at https://github.com/PKU-AI-Edge/I2C.