Linear Multi-Resource Allocation with Semi-Bandit Feedback
We study an idealised sequential resource allocation problem. In each time step the learner chooses an allocation of several resource types between a number of tasks. Assigning more resources to a task increases the probability that it is completed. The problem is challenging because the alignment of the tasks to the resource types is unknown and the feedback is noisy. Our main contribution is the new setting and an algorithm with nearly-optimal regret analysis. Along the way we draw connections to the problem of minimising regret for stochastic linear bandits with heteroscedastic noise. We also present some new results for stochastic linear bandits on the hypercube that significantly out-performs existing work, especially in the sparse case.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Censored Semi-Bandits for Resource Allocation
We consider the problem of sequentially allocating resources in a censored semi-bandits setup, where the learner allocates resources at each step to the arms and observes loss. The loss depends on two hidden parameters, …
Multi-Armed BanditsCensored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback
In this paper, we study censored Semi-Bandits, a novel variant of the semi-bandits problem. The learner is assumed to have a fixed amount of resources, which it allocates to the arms at each time step. The loss observed …
Multi-Armed BanditsOptimal Resource Allocation with Semi-Bandit Feedback
We study a sequential resource allocation problem involving a fixed number of recurring jobs. At each time-step the manager should distribute available resources among the jobs in order to maximise the expected number of…
Combinatorial Multi-armed Bandits for Resource Allocation
We study the sequential resource allocation problem where a decision maker repeatedly allocates budgets between resources. Motivating examples include allocating limited computing time or wireless spectrum bands to multi…
Multi-Armed BanditsA Better Resource Allocation Algorithm with Semi-Bandit Feedback
We study a sequential resource allocation problem between a fixed number of arms. On each iteration the algorithm distributes a resource among the arms in order to maximize the expected success rate. Allocating more of t…