Abstract:We study distributed contextual linear bandits with stochastic contexts, where N agents act cooperatively to solve a linear bandit-optimization problem with d-dimensional features. For this problem, we propose a distributed batch elimination version of the LinUCB algorithm, DisBE-LUCB, where the agents share information among each other through a central server. We prove that over T rounds (N T actions in total) the communication cost of DisBE-LUCB is only Õ(dN ) and its regret is at most Õ( √ dN T ), which is… Show more
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