89 citations · 357 across the 36 of their papers we have counts for
37 papers
Robust Driving Policy Learning with Guided Meta Reinforcement Learning
Kanghoon Lee, Jiachen Li, David Isele +3
Although deep reinforcement learning (DRL) has shown promising results for autonomous navigation in interactive traffic scenarios, existing work typically adopts a fixed behavior p…
Computing Algorithm for an Equilibrium of the Generalized Stackelberg Game
Jaeyeon Jo, Jihwan Yu, Jinkyoo Park
The generalized Stackelberg game (single-leader multi-follower game) is intricately intertwined with the interaction between a leader and followers (hierarchical interaction)…
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…
A Neural Separation Algorithm for the Rounded Capacity Inequalities
Hyeonah Kim, Jinkyoo Park, Changhyun Kwon
The cutting plane method is a key technique for successful branch-and-cut and branch-price-and-cut algorithms that find the exact optimal solutions for various vehicle routing prob…
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…
Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences
Minsu Kim, Federico Berto, Sungsoo Ahn +1
We study the problem of optimizing biological sequences, e.g., proteins, DNA, and RNA, to maximize a black-box score function that is only evaluated in an offline dataset. We propo…
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…
Learning context-aware adaptive solvers to accelerate quadratic programming
Haewon Jung, Junyoung Park, Jinkyoo Park
Convex quadratic programming (QP) is an important sub-field of mathematical optimization. The alternating direction method of multipliers (ADMM) is a successful method to solve QP.…
Bayesian Convolutional Deep Sets with Task-Dependent Stationary Prior
Yohan Jung, Jinkyoo Park
Convolutional deep sets are the architecture of a deep neural network (DNN) that can model stationary stochastic process. This architecture uses the kernel smoother and the DNN to…
EvolveHypergraph: Group-Aware Dynamic Relational Reasoning for Trajectory Prediction
Jiachen Li, Chuanbo Hua, Jinkyoo Park +3
While the modeling of pair-wise relations has been widely studied in multi-agent interacting systems, its ability to capture higher-level and larger-scale group-wise activities is…
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…
Neural Solvers for Fast and Accurate Numerical Optimal Control
Federico Berto, Stefano Massaroli, Michael Poli +1
Synthesizing optimal controllers for dynamical systems often involves solving optimization problems with hard real-time constraints. These constraints determine the class of numeri…
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…