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20212026
most citedThe Impact of Task Underspecification in Evaluating Deep Reinforcement Learning

4 citations · 13 across the 12 of their papers we have counts for

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5 papers · 1 filter

cs.LG2025

Learning to Segment for Vehicle Routing Problems

Wenbin Ouyang, Sirui Li, Yining Ma +1

Iterative heuristics are widely recognized as state-of-the-art for Vehicle Routing Problems (VRPs). In this work, we exploit a critical observation: a large portion of the solution…

cs.LG20243 cited

Towards Foundation Models for Mixed Integer Linear Programming

Sirui Li, Janardhan Kulkarni, Ishai Menache +2

Mixed Integer Linear Programming (MILP) is essential for modeling complex decision-making problems but faces challenges in computational tractability and requires expert formulatio…

cs.LG2024

Model-Based Transfer Learning for Contextual Reinforcement Learning

Jung-Hoon Cho, Vindula Jayawardana, Sirui Li +1

Deep reinforcement learning (RL) is a powerful approach to complex decision making. However, one issue that limits its practical application is its brittleness, sometimes failing t…

cs.LG20224 cited

The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning

Vindula Jayawardana, Catherine Tang, Sirui Li +2

Evaluations of Deep Reinforcement Learning (DRL) methods are an integral part of scientific progress of the field. Beyond designing DRL methods for general intelligence, designing…

cs.LG20212 cited

Learning to Delegate for Large-scale Vehicle Routing

Sirui Li, Zhongxia Yan, Cathy Wu

Vehicle routing problems (VRPs) form a class of combinatorial problems with wide practical applications. While previous heuristic or learning-based works achieve decent solutions o…