116 citations · 302 across the 14 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
XOR-Sampling for Network Design with Correlated Stochastic Events
Xiaojian Wu, Yexiang Xue, Bart Selman +1
Many network optimization problems can be formulated as stochastic network design problems in which edges are present or absent stochastically. Furthermore, protective actions can…
Algorithm Portfolio Design: Theory vs. Practice
Carla P. Gomes, Bart Selman
Stochastic algorithms are among the best for solving computationally hard search and reasoning problems. The runtime of such procedures is characterized by a random variable. Diffe…