April 2, 2024, 7:42 p.m. | Kimiya Saadat, Richard Zhao

cs.LG updates on arXiv.org arxiv.org

arXiv:2210.05014v3 Announce Type: cross
Abstract: In recent years, Monte Carlo tree search (MCTS) has achieved widespread adoption within the game community. Its use in conjunction with deep reinforcement learning has produced success stories in many applications. While these approaches have been implemented in various games, from simple board games to more complicated video games such as StarCraft, the use of deep neural networks requires a substantial training period. In this work, we explore on-line adaptivity in MCTS without requiring pre-training. …

abstract adoption applications arxiv board board games community cs.ai cs.lg game games reinforcement reinforcement learning search simple stories success success stories tree type

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Robotics Technician - 3rd Shift

@ GXO Logistics | Perris, CA, US, 92571