Multi-layered network exploration (MuLaNE) problem is an important problem abstracted from many applications. In MuLaNE, there are multiple network layers where each node has an importance weight and each layer is explored by a random walk. The MuLaNE task is to allocate total random walk budget $B$ into each network layer so that the total weights of the unique nodes visited by random walks are maximized. We systematically study this problem from offline optimization to online learning. For the offline optimization setting where the network structure and node weights are known, we provide greedy based constant-ratio approximation algorithms for overlapping networks, and greedy or dynamic-programming based optimal solutions for non-overlapping networks. For the online learning setting, neither the network structure nor the node weights are known initially. We adapt the combinatorial multi-armed bandit framework and design algorithms to learn random walk related parameters and node weights while optimizing the budget allocation in multiple rounds, and prove that they achieve logarithmic regret bounds. Finally, we conduct experiments on a real-world social network dataset to validate our theoretical results.
CODA: Coordination via On-Policy Diffusion for Multi-Agent Offline Reinforcement Learning
Inephany Ltd（Inephany有限公司）；Department of Statistics, University of Oxford（牛津大学统计系）；University of Cape Town Cape Town, South Africa（南非开普敦大学）；The University of Edinburgh（爱丁堡大学）
Agent-Controller Representations: Principled Offline RL with Rich Exogenous Information
University of Alberta（阿尔伯塔大学）；Amii（阿尔伯塔机器智能研究所）；University of Montreal（蒙特利尔大学）；Quebec AI Institute（魁北克人工智能研究所）；Beijing Institute of Technology（北京理工大学）；Meta；Microsoft Research（微软研究院）；McGill University（麦吉尔大学）
Diffusion Policies for Out-of-Distribution Generalization in Offline Reinforcement Learning
Department of Computer Engineering, Bogazici University（计算机工程系，博雅大学）；SISReC, OTRI, Osaka University（SISReC、OTRI、大阪大学）；Department of Computer Science, Ozyegin University（计算机科学系，奥兹格伦大学）
Multiple Similarity Drug-Target Interaction Prediction with Random Walks and Matrix Factorization
Chongqing University of Posts and Telecommunications（重庆邮电大学）；Aristotle University of Thessaloniki（塞萨洛尼基亚里士多德大学）；Paris-Saclay University（巴黎萨克雷大学）；CentraleSupélec（巴黎中央理工-高等电力学院）；Inria（法国国家信息与自动化研究所）
