1 code implementation • 12 Mar 2024 • Huijie Tang, Federico Berto, Jinkyoo Park
We first propose a selective communication block to gather richer information for better agent coordination within multi-agent environments and train the model with a Q-learning-based algorithm.
Multi-Agent Path Finding Multi-agent Reinforcement Learning +1
1 code implementation • 23 Feb 2024 • Huijie Tang, Federico Berto, Zihan Ma, Chuanbo Hua, Kyuree Ahn, Jinkyoo Park
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.
1 code implementation • 29 Jun 2023 • Federico Berto, Chuanbo Hua, Junyoung Park, Minsu Kim, Hyeonah Kim, Jiwoo Son, Haeyeon Kim, Joungho Kim, Jinkyoo Park
To address these challenges, we introduce RL4CO, a unified Reinforcement Learning (RL) for Combinatorial Optimization (CO) library.
1 code implementation • NeurIPS 2023 • Minsu Kim, Federico Berto, Sungsoo Ahn, Jinkyoo Park
The subsequent stage involves bootstrapping, which augments the training dataset with self-generated data labeled by a proxy score function.
1 code implementation • 26 Nov 2022 • Michael Poli, Stefano Massaroli, Federico Berto, Jinykoo Park, Tri Dao, Christopher Ré, Stefano Ermon
Instead, this work introduces a blueprint for frequency domain learning through a single transform: transform once (T1).
no code implementations • 1 Jun 2022 • Junyoung Park, Federico Berto, Arec Jamgochian, Mykel J. Kochenderfer, Jinkyoo Park
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 their context.
2 code implementations • 26 May 2022 • Haeyeon Kim, Minsu Kim, Federico Berto, Joungho Kim, Jinkyoo Park
In this paper, we present DevFormer, a novel transformer-based architecture for addressing the complex and computationally demanding problem of hardware design optimization.
1 code implementation • NeurIPS Workshop DLDE 2021 • Federico Berto, Stefano Massaroli, Michael Poli, Jinkyoo Park
Synthesizing optimal controllers for dynamical systems often involves solving optimization problems with hard real-time constraints.