Abstract
Multi-target search is a problem where multiple targets that required to be search, mayor may not, move in an environment. Thompson Sampling, a method based on Beta distribution, was introduced to solve such a problem. Compared with random selection, which allows the robots to move randomly, its effectiveness under different concentrated targets environment is proved. According to the analysis, the adaptability of Thompson Sampling is a great advantage compared to other method. These results shed light for in multi-target search problem.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
