论文标题
多代理多军强盗问题的动态观察策略
A Dynamic Observation Strategy for Multi-agent Multi-armed Bandit Problem
论文作者
论文摘要
我们定义和分析了多工具的强盗问题，决策代理可以在线性观察成本下观察其邻居的选择和回报。邻居由网络图定义，该网络图编码系统的固有观察约束。我们定义了与观察相关的成本，以便在每个实例中，代理人都会观察到它会感到持续的观察后悔。我们设计了一种抽样算法和每个代理的观察方案，以最大程度地减少预期的累积抽样后悔和预期的累积观察后悔，从而最大程度地提高自己的预期累积奖励。对于我们提出的协议，我们证明了总累积遗憾是对数的界限。我们使用数值模拟来验证分析界限的准确性。
We define and analyze a multi-agent multi-armed bandit problem in which decision-making agents can observe the choices and rewards of their neighbors under a linear observation cost. Neighbors are defined by a network graph that encodes the inherent observation constraints of the system. We define a cost associated with observations such that at every instance an agent makes an observation it receives a constant observation regret. We design a sampling algorithm and an observation protocol for each agent to maximize its own expected cumulative reward through minimizing expected cumulative sampling regret and expected cumulative observation regret. For our proposed protocol, we prove that total cumulative regret is logarithmically bounded. We verify the accuracy of analytical bounds using numerical simulations.
