  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.

Optimization and Control;Machine Learning