Learning Individually Inferred Communication for Multi-Agent Cooperation (2006.06455v2)

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.

Learning Individually Inferred Communication for Multi-Agent Cooperation

The paper "Learning Individually Inferred Communication for Multi-Agent Cooperation" presents an innovative approach to multi-agent reinforcement learning (MARL) that prioritizes efficiency in agent communication. Traditional methods often rely on broadcast communication, where information is broadcasted to all agents, leading to substantial bandwidth consumption and potential information redundancy. This paper introduces a novel communication protocol known as Individually Inferred Communication (I2C), which allows agents to selectively communicate with others based on inferred necessity.

Key Contributions

The primary contribution of this work is the development of the I2C model, which harnesses causal inference to enable agents to learn a prior for communication necessity. This approach leverages a feed-forward neural network to map an agent's local observation to a belief about which other agents it should communicate with. I2C is designed to optimize the communication process by only engaging in interactions deemed necessary, thereby reducing communication overhead and potentially enhancing cooperative strategies.

Numerical Results and Empirical Validation

Through empirical testing across various cooperative multi-agent scenarios, including cooperative navigation, predator-prey dynamics, and complex traffic junctions, the paper demonstrates that I2C achieves superior performance over traditional methods like IC3Net and TarMAC, as well as the baseline MADDPG. For instance, in cooperative navigation tasks, I2C attained a reward of -0.73 compared to -1.26 achieved by MADDPG, showcasing its efficiency in strategy formation and target selection.

Implications and Future Directions

The adoption of I2C within the field of MARL bears significant implications for both theoretical and applied contexts. By curbing unnecessary communication, I2C not only enhances computational efficiency but also aligns closely with real-world constraints on bandwidth and communication range. The model's ability to infer the causal relationship between agents' actions and communication necessity underscores its potential to be adapted across diverse real-world applications, from autonomous vehicles to smart grid management.

Looking forward, this approach suggests avenues for further research, particularly in the integration of more complex causal inference mechanisms and adaptive learning frameworks that dynamically adjust to evolving multi-agent environments. Additionally, the compatibility of I2C with various CTDE-based frameworks opens up collaborations for integrating this protocol into existing systems to further validate and refine its efficacy.

Conclusion

Overall, the Individually Inferred Communication model represents a significant stride toward efficient and scalable multi-agent cooperation by focusing on necessity-driven communication. This paper provides a comprehensive framework that combines causal inference with MARL, offering both numerical prowess and practical applicability, reflecting a promising direction for advancing autonomous agent collaboration in complex environments.