Learning Attentional Communication for Multi-Agent Cooperation (1805.07733v3)

Abstract: Communication could potentially be an effective way for multi-agent cooperation. However, information sharing among all agents or in predefined communication architectures that existing methods adopt can be problematic. When there is a large number of agents, agents cannot differentiate valuable information that helps cooperative decision making from globally shared information. Therefore, communication barely helps, and could even impair the learning of multi-agent cooperation. Predefined communication architectures, on the other hand, restrict communication among agents and thus restrain potential cooperation. To tackle these difficulties, in this paper, we propose an attentional communication model that learns when communication is needed and how to integrate shared information for cooperative decision making. Our model leads to efficient and effective communication for large-scale multi-agent cooperation. Empirically, we show the strength of our model in a variety of cooperative scenarios, where agents are able to develop more coordinated and sophisticated strategies than existing methods.

Learning Attentional Communication for Multi-Agent Cooperation

This paper presents an attentional communication framework designed for enhancing cooperation in multi-agent reinforcement learning (MARL). The critical issue addressed by the authors is the inefficiency and limitations of both global information sharing and predefined communication architectures in MARL when many agents are involved. The proposed method, termed Attentional Communication Model (ATOC), dynamically determines the necessity of inter-agent communication, aiming to selectively integrate pertinent information that supports cooperative decision-making.

Key Contributions

The main contribution of this work is the introduction of a novel attentional communication mechanism that operates efficiently within large-scale and partially observable multi-agent environments. Unlike prior models such as DIAL, CommNet, and BiCNet that either rely on global communication or static communication structures, ATOC employs an attention unit to identify relevant agents with whom to communicate. This attention model is calibrated to discern when communication is beneficial, forming transient communication groups that can tactically facilitate information exchange. By exploiting a communication channel implemented via a bi-directional LSTM unit, the model allows the sharing of encoded local observations and action intentions, thereby fostering complex cooperative strategies among agents.

Empirical Evaluation

The effectiveness of ATOC is verified empirically across multiple scenarios, showcasing superior performance over existing approaches like DDPG, CommNet, and BiCNet. In the cooperative navigation task, ATOC demonstrated improved coordination and achieved higher efficiency with fewer collisions and a higher percentage of occupied landmarks. The cooperative pushball task highlighted the sophisticated decision-making capabilities enabled by attentional communication, with ATOC agents adopting strategic behaviors such as controlling the ball's direction and movement to the target location. Furthermore, the predator-prey scenario emphasized the competitive edge of ATOC in heterogeneous environments, further demonstrating the adaptability of the learned policies.

Implications and Future Work

The introduction of attention-driven communication within MARL presents significant implications for the development of more scalable and adaptable cooperative systems. By reducing the communication overhead and selectively enabling interactions among agents only when necessary, the proposed model enhances decision-making efficiency while conserving resources. This has particular relevance in real-world applications involving autonomous vehicles, drone swarms, and smart grid management.

Future work may explore the extension of this attentional communication framework to incorporate more complex agent dynamics and heterogeneous environments. Additionally, investigating the integration of this model with other emerging MARL architectures could unlock further improvements in cooperative AI. The scalability of the approach could also be tested in other domains, offering a more comprehensive understanding of its applicability in diverse scenarios.