23 citations · 32 across the 11 of their papers we have counts for
7 papers · 1 filter
Synthesizable Molecular Generation via Soft-constrained GFlowNets with Rich Chemical Priors
Hyeonah Kim, Minsu Kim, Celine Roget +5
The application of generative models for experimental drug discovery campaigns is severely limited by the difficulty of designing molecules de novo that can be synthesized in pract…
Improved Off-policy Reinforcement Learning in Biological Sequence Design
Hyeonah Kim, Minsu Kim, Taeyoung Yun +4
Designing biological sequences with desired properties is challenging due to vast search spaces and limited evaluation budgets. Although reinforcement learning methods use proxy mo…
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
Minsu Kim, Sanghyeok Choi, Hyeonah Kim +3
We present the Generative Flow Ant Colony Sampler (GFACS), a novel meta-heuristic method that hierarchically combines amortized inference and parallel stochastic search. Our method…
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization
Jiwoo Son, Minsu Kim, Hyeonah Kim +1
This paper proposes Meta-SAGE, a novel approach for improving the scalability of deep reinforcement learning models for combinatorial optimization (CO) tasks. Our method adapts pre…
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
Federico Berto, Chuanbo Hua, Junyoung Park +30
Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation. Deep reinforcement lear…
Equity-Transformer: Solving NP-hard Min-Max Routing Problems as Sequential Generation with Equity Context
Jiwoo Son, Minsu Kim, Sanghyeok Choi +2
Min-max routing problems aim to minimize the maximum tour length among multiple agents by having agents conduct tasks in a cooperative manner. These problems include impactful real…
Symmetric Replay Training: Enhancing Sample Efficiency in Deep Reinforcement Learning for Combinatorial Optimization
Hyeonah Kim, Minsu Kim, Sungsoo Ahn +1
Deep reinforcement learning (DRL) has significantly advanced the field of combinatorial optimization (CO). However, its practicality is hindered by the necessity for a large number…