Task Partitioning in Swarm Robotics as a Multi-Armed Bandit Problem
نویسندگان
چکیده
The information provided is the sole responsibility of the authors and does not necessarily reflect the opinion of the members of IRIDIA. The authors take full responsibility for any copyright breaches that may result from publication of this paper in the IRIDIA – Technical Report Series. IRIDIA is not responsible for any use that might be made of data appearing in this publication.
منابع مشابه
Multi-armed Bandit Formulation of the Task Partitioning Problem in Swarm Robotics
Task partitioning is a way of organizing work consisting in the decomposition of a task into smaller sub-tasks that can be tackled separately. Task partitioning can be beneficial in terms of reduction of physical interference, increase of efficiency, higher parallelism, and exploitation of specialization. However, task partitioning also entails costs in terms of coordination efforts and overhea...
متن کاملFinite dimensional algorithms for the hidden Markov model multi-armed bandit problem
The multi-arm bandit problem is widely used in scheduling of traffic in broadband networks, manufacturing systems and robotics. This paper presents a finite dimensional optimal solution to the multi-arm bandit problem for Hidden Markov Models. The key to solving any multi-arm bandit problem is to compute the Gittins index. In this paper a finite dimensional algorithm is presented which exactly ...
متن کاملA quality assuring multi-armed bandit crowdsourcing mechanism with incentive compatible learning
We develop a novel multi-armed bandit (MAB) mechanism for the problem of selecting a subset of crowd workers to achieve an assured accuracy for each binary labelling task in a cost optimal way. This problem is challenging because workers have unknown qualities and strategic costs.
متن کاملBudgeted Learning, Part I: The Multi-Armed Bandit Case
We introduce and motivate the task of learning under a budget. We focus on a basic problem in this space: selecting the optimal bandit after a period of experimentation in a multi-armed bandit setting, where each experiment is costly, our total costs cannot exceed a fixed pre-specified budget, and there is no reward collection during the learning period. We address the computational complexity ...
متن کاملMulti-fidelity Bandit Optimisation∗
In many scientific and engineering applications, we are tasked with the optimisation of an expensive to evaluate black box function. Traditional methods for this problem assume just the availability of this single function. However, in many cases, cheap approximations may be available. For example, in optimal policy search in robotics, the expensive real world behaviour of a robot can be approx...
متن کامل