Learning in Distributed Contextual Linear Bandits Without Sharing the Context
Contextual linear bandits is a rich and theoretically important model that has many practical applications. Recently, this setup gained a lot of interest in applications over wireless where communication constraints can be a performance bottleneck, especially when the contexts come from a large $d$-dimensional space. In this paper, we consider a distributed memoryless contextual linear bandit learning problem, where the agents who observe the contexts and take actions are geographically separated from the learner who performs the learning while not seeing the contexts. We assume that contexts are generated from a distribution and propose a method that uses $\approx 5d$ bits per context for the case of unknown context distribution and $0$ bits per context if the context distribution is known, while achieving nearly the same regret bound as if the contexts were directly observable. The former bound improves upon existing bounds by a $\log(T)$ factor, where $T$ is the length of the horizon, while the latter achieves information theoretical tightness.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Federated Neural Bandits
Recent works on neural contextual bandits have achieved compelling performances due to their ability to leverage the strong representation power of neural networks (NNs) for reward prediction. Many applications of contex…
Multi-Armed BanditsMulti-Agent Learning with Heterogeneous Linear Contextual Bandits
As trained intelligent systems become increasingly pervasive, multiagent learning has emerged as a popular framework for studying complex interactions between autonomous agents. Yet, a formal understanding of how and whe…
Distributed Multi-Task Learning for Stochastic Bandits with Context Distribution and Stage-wise Constraints
We present conservative distributed multi-task learning in stochastic linear contextual bandits with heterogeneous agents. This extends conservative linear bandits to a distributed setting where M agents tackle different…
Multi-Armed BanditsMulti-Task LearningStock Market PredictionCommunication Efficient Distributed Learning for Kernelized Contextual Bandits
We tackle the communication efficiency challenge of learning kernelized contextual bandits in a distributed setting. Despite the recent advances in communication-efficient distributed bandit learning, existing solutions …
Multi-Armed BanditsDistributed Contextual Linear Bandits with Minimax Optimal Communication Cost
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 over the course of $T$ rounds. For t…