all AI news
A Reinforcement Learning based Reset Policy for CDCL SAT Solvers
April 8, 2024, 4:42 a.m. | Chunxiao Li (John), Charlie Liu (John), Jonathan Chung (John), Zhengyang (John), Lu, Piyush Jha, Vijay Ganesh
cs.LG updates on arXiv.org arxiv.org
Abstract: Restart policy is an important technique used in modern Conflict-Driven Clause Learning (CDCL) solvers, wherein some parts of the solver state are erased at certain intervals during the run of the solver. In most solvers, variable activities are preserved across restart boundaries, resulting in solvers continuing to search parts of the assignment tree that are not far from the one immediately prior to a restart. To enable the solver to search possibly "distant" parts of …
abstract arxiv conflict cs.ai cs.lg cs.lo modern policy reinforcement reinforcement learning solver state type
More from arxiv.org / cs.LG updates on arXiv.org
Jobs in AI, ML, Big Data
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
Senior Data Science Analyst- ML/DL/LLM
@ Mayo Clinic | Jacksonville, FL, United States
Machine Learning Research Scientist, Robustness and Uncertainty
@ Nuro, Inc. | Mountain View, California (HQ)