89 citations · 357 across the 36 of their papers we have counts for
25 papers · 1 filter
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…
Neuro CROSS exchange: Learning to CROSS exchange to solve realistic vehicle routing problems
Minjun Kim, Junyoung Park, Jinkyoo Park
CROSS exchange (CE), a meta-heuristic that solves various vehicle routing problems (VRPs), improves the solutions of VRPs by swapping the sub-tours of the vehicles. Inspired by CE,…
Meta-SysId: A Meta-Learning Approach for Simultaneous Identification and Prediction
Junyoung Park, Federico Berto, Arec Jamgochian +2
In this paper, we propose Meta-SysId, a meta-learning approach to model sets of systems that have behavior governed by common but unknown laws and that differentiate themselves by…
DevFormer: A Symmetric Transformer for Context-Aware Device Placement
Haeyeon Kim, Minsu Kim, Federico Berto +2
In this paper, we present DevFormer, a novel transformer-based architecture for addressing the complex and computationally demanding problem of hardware design optimization. Despit…
Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization
Minsu Kim, Junyoung Park, Jinkyoo Park
Deep reinforcement learning (DRL)-based combinatorial optimization (CO) methods (i.e., DRL-NCO) have shown significant merit over the conventional CO solvers as DRL-NCO is capable…
Learning Collaborative Policies to Solve NP-hard Routing Problems
Minsu Kim, Jinkyoo Park, Joungho Kim
Recently, deep reinforcement learning (DRL) frameworks have shown potential for solving NP-hard routing problems such as the traveling salesman problem (TSP) without problem-specif…
Continuous-Depth Neural Models for Dynamic Graph Prediction
Michael Poli, Stefano Massaroli, Clayton M. Rabideau +4
We introduce the framework of continuous-depth graph neural networks (GNNs). Neural graph differential equations (Neural GDEs) are formalized as the counterpart to GNNs where the i…
Neural Hybrid Automata: Learning Dynamics with Multiple Modes and Stochastic Transitions
Michael Poli, Stefano Massaroli, Luca Scimeca +6
Effective control and prediction of dynamical systems often require appropriate handling of continuous-time and discrete, event-triggered processes. Stochastic hybrid systems (SHSs…
Differentiable Multiple Shooting Layers
Stefano Massaroli, Michael Poli, Sho Sonoda +4
We detail a novel class of implicit neural models. Leveraging time-parallel methods for differential equations, Multiple Shooting Layers (MSLs) seek solutions of initial value prob…
Learning Stochastic Optimal Policies via Gradient Descent
Stefano Massaroli, Michael Poli, Stefano Peluchetti +3
We systematically develop a learning-based treatment of stochastic optimal control (SOC), relying on direct optimization of parametric control policies. We propose a derivation of…
ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning
Junyoung Park, Sanjar Bakhtiyar, Jinkyoo Park
We propose ScheduleNet, a RL-based real-time scheduler, that can solve various types of multi-agent scheduling problems. We formulate these problems as a semi-MDP with episodic rew…
Convergent Graph Solvers
Junyoung Park, Jinhyun Choo, Jinkyoo Park
We propose the convergent graph solver (CGS), a deep learning method that learns iterative mappings to predict the properties of a graph system at its stationary state (fixed point…
Cooperative and Competitive Biases for Multi-Agent Reinforcement Learning
Heechang Ryu, Hayong Shin, Jinkyoo Park
Training a multi-agent reinforcement learning (MARL) algorithm is more challenging than training a single-agent reinforcement learning algorithm, because the result of a multi-agen…
TorchDyn: A Neural Differential Equations Library
Michael Poli, Stefano Massaroli, Atsushi Yamashita +2
Continuous-depth learning has recently emerged as a novel perspective on deep learning, improving performance in tasks related to dynamical systems and density estimation. Core to…
REMAX: Relational Representation for Multi-Agent Exploration
Heechang Ryu, Hayong Shin, Jinkyoo Park
Training a multi-agent reinforcement learning (MARL) model with a sparse reward is generally difficult because numerous combinations of interactions among agents induce a certain o…
Hypersolvers: Toward Fast Continuous-Depth Models
Michael Poli, Stefano Massaroli, Atsushi Yamashita +2
The infinite-depth paradigm pioneered by Neural ODEs has launched a renaissance in the search for novel dynamical system-inspired deep learning primitives; however, their utilizati…
Stable Neural Flows
Stefano Massaroli, Michael Poli, Michelangelo Bin +3
We introduce a provably stable variant of neural ordinary differential equations (neural ODEs) whose trajectories evolve on an energy functional parametrised by a neural network. S…
Dissecting Neural ODEs
Stefano Massaroli, Michael Poli, Jinkyoo Park +2
Continuous deep learning architectures have recently re-emerged as Neural Ordinary Differential Equations (Neural ODEs). This infinite-depth approach theoretically bridges the gap…
Scalable Hybrid HMM with Gaussian Process Emission for Sequential Time-series Data Clustering
Yohan Jung, Jinkyoo Park
Hidden Markov Model (HMM) combined with Gaussian Process (GP) emission can be effectively used to estimate the hidden state with a sequence of complex input-output relational obser…
Graph Neural Ordinary Differential Equations
Michael Poli, Stefano Massaroli, Junyoung Park +3
We introduce the framework of continuous--depth graph neural networks (GNNs). Graph neural ordinary differential equations (GDEs) are formalized as the counterpart to GNNs where th…
Multi-Agent Actor-Critic with Hierarchical Graph Attention Network
Heechang Ryu, Hayong Shin, Jinkyoo Park
Most previous studies on multi-agent reinforcement learning focus on deriving decentralized and cooperative policies to maximize a common reward and rarely consider the transferabi…