116 citations · 495 across the 29 of their papers we have counts for
29 papers
Graph Value Iteration
Dieqiao Feng, Carla P. Gomes, Bart Selman
In recent years, deep Reinforcement Learning (RL) has been successful in various combinatorial search domains, such as two-player games and scientific discovery. However, directly…
Left Heavy Tails and the Effectiveness of the Policy and Value Networks in DNN-based best-first search for Sokoban Planning
Dieqiao Feng, Carla Gomes, Bart Selman
Despite the success of practical solvers in various NP-complete domains such as SAT and CSP as well as using deep reinforcement learning to tackle two-player games such as Go, cert…
A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances
Dieqiao Feng, Carla P. Gomes, Bart Selman
In recent years, we have witnessed tremendous progress in deep reinforcement learning (RL) for tasks such as Go, Chess, video games, and robot control. Nevertheless, other combinat…
Zero Training Overhead Portfolios for Learning to Solve Combinatorial Problems
Yiwei Bai, Wenting Zhao, Carla P. Gomes
There has been an increasing interest in harnessing deep learning to tackle combinatorial optimization (CO) problems in recent years. Typical CO deep learning approaches leverage t…
Disentangled Variational Autoencoder based Multi-Label Classification with Covariance-Aware Multivariate Probit Model
Junwen Bai, Shufeng Kong, Carla Gomes
Multi-label classification is the challenging task of predicting the presence and absence of multiple targets, involving representation learning and label correlation modeling. We…
Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning
Dieqiao Feng, Carla P. Gomes, Bart Selman
Despite significant progress in general AI planning, certain domains remain out of reach of current AI planning systems. Sokoban is a PSPACE-complete planning task and represents o…