Author name cluster

Kyuree Ahn

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4 papers

2 author rows

NeurIPS Conference 2025 Conference Paper

PARCO: Parallel AutoRegressive Models for Multi-Agent Combinatorial Optimization

Federico Berto
Chuanbo Hua
Laurin Luttmann
Jiwoo Son
Junyoung Park
Kyuree Ahn
Changhyun Kwon
Lin Xie

Combinatorial optimization problems involving multiple agents are notoriously challenging due to their NP-hard nature and the necessity for effective agent coordination. Despite advancements in learning-based methods, existing approaches often face critical limitations, including suboptimal agent coordination, poor generalization, and high computational latency. To address these issues, we propose PARCO (Parallel AutoRegressive Combinatorial Optimization), a general reinforcement learning framework designed to construct high-quality solutions for multi-agent combinatorial tasks efficiently. To this end, PARCO integrates three key novel components: (1) transformer-based communication layers to enable effective agent collaboration during parallel solution construction, (2) a multiple pointer mechanism for low-latency, parallel agent decision-making, and (3) priority-based conflict handlers to resolve decision conflicts via learned priorities. We evaluate PARCO in multi-agent vehicle routing and scheduling problems, where our approach outperforms state-of-the-art learning methods, demonstrating strong generalization ability and remarkable computational efficiency. We make our source code publicly available to foster future research: https: //github. com/ai4co/parco.

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AAMAS Conference 2024 Conference Paper

HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding

Huijie Tang
Federico Berto
Zihan Ma
Chuanbo Hua
Kyuree Ahn
Jinkyoo Park

Large-scale multi-agent pathfinding (MAPF) presents significant challenges in several areas. As systems grow in complexity with a multitude of autonomous agents operating simultaneously, efficient and collision-free coordination becomes paramount. Traditional algorithms often fall short in scalability, especially in intricate scenarios. Reinforcement Learning (RL) has shown potential to address the intricacies of MAPF; however, it has also been shown to struggle with scalability, demanding intricate implementation, lengthy training, and often exhibiting unstable convergence, limiting its practical application. In this paper, we introduce Heuristics-Informed Multi- Agent Pathfinding (HiMAP), a novel scalable approach that employs imitation learning with heuristic guidance in a decentralized manner. We train on small-scale instances using a heuristic policy as a teacher that maps each single agent observation information to an action probability distribution. During pathfinding, we adopt several inference techniques to improve performance. With a simple training scheme and implementation, HiMAP demonstrates competitive results in terms of success rate and scalability in the field of imitation-learning-only MAPF, showing the potential of imitation-learning-only MAPF equipped with inference techniques.

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IROS Conference 2024 Conference Paper

NLNS-MASPF for solving Multi-Agent scheduling and Path-Finding

Heemang Park
Kyuree Ahn
Jinkyoo Park

In this work, we propose a novel method, NLNS-MASPF, to solve the Multi-Agent Scheduling and Pathfinding (MASPF) problem. The problem exhibits a bi-level structure, consisting of High-level Scheduling and Low-level Pathfinding. Our method applies a graph neural network in the high-level scheduling process and utilizes a MAPF solver with a schedule segmenting technique in the low-level pathfinding process. Through these approaches, NLNS-MASPF has experimentally demonstrated superior performance compared to the previous state-of-the-art MASPF algorithm, LNS-PBS, in solving the MASPF problem.

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IROS Conference 2023 Conference Paper

Learning to Schedule in Multi-Agent Pathfinding

Kyuree Ahn
Heemang Park
Jinkyoo Park

In this work, we consider the problem of allocating tasks and planning paths for multiple robots to operate cooper-atively. We formulate the problem as a bi-level optimization that involves optimizing the scheduling of robots and the collision-free path for each robot. To address the complexity of the environment with obstacles, we introduce a congestion-aware state representation technique with the aid of graph neural networks. We also derive a joint scheduling policy using multi-agent reinforcement learning, and we propose an additional auxiliary loss that promotes coordination among the robots. Through empirical evaluation, we demonstrate the effectiveness of our approach in solving the combined task allocation and path planning problem in a cooperative multi-robot system.

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