可自我配置的网格网络用于可扩展的分布式子模带提优化
Self-Configurable Mesh-Networks for Scalable Distributed Submodular Bandit Optimization
AI总结：
本文提出了一种可自我配置的网格网络方法，通过限制信息中继和消息大小，实现分布式子模带提优化的可扩展性和接近最优性。
摘要：
我们研究如何在带宽、数据速率和连通性等现实通信限制下扩展分布式带提子模协调。我们受到多智能体在未知、部分可观测和资源有限环境中的主动情境感知任务的启发，其中智能体必须通过智能体到智能体的通信进行协调。我们的方法通过（i）限制信息中继仅限于单跳通信和（ii）保持智能体之间的消息小，让每个智能体仅传输其自身的行为信息来实现可扩展性。尽管这些信息访问限制，我们的方法通过分布式在线带提优化实现接近最优的行为协调，受智能体带宽限制。特别是，我们的方法享有任何时间子最优界，即使对于任意网络拓扑，即使断开，该界也是严格正的。为了证明该界，我们定义了协调的价值（VoC），一个信息论度量，量化了每个智能体对其邻居信息访问的收益。我们在模拟中验证了我们方法的可扩展性和接近最优性：观察到它收敛更快，优于带提子模协调的基准，并且甚至可以优于拥有环境先验知识的基准。
英文摘要：
We study how to scale distributed bandit submodular coordination under realistic communication constraints in bandwidth, data rate, and connectivity. We are motivated by multi-agent tasks of active situational awareness in unknown, partially-observable, and resource-limited environments, where the agents must coordinate through agent-to-agent communication. Our approach enables scalability by (i) limiting information relays to only one-hop communication and (ii) keeping inter-agent messages small, having each agent transmit only its own action information. Despite these information-access restrictions, our approach enables near-optimal action coordination by optimizing the agents' communication neighborhoods over time, through distributed online bandit optimization, subject to the agents' bandwidth constraints. Particularly, our approach enjoys an anytime suboptimality bound that is also strictly positive for arbitrary network topologies, even disconnected. To prove the bound, we define the Value of Coordination (VoC), an information-theoretic metric that quantifies for each agent the benefit of information access to its neighbors. We validate in simulations the scalability and near-optimality of our approach: it is observed to converge faster, outperform benchmarks for bandit submodular coordination, and can even outperform benchmarks that are privileged with a priori knowledge of the environment.
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