Search Results for author: Federico Berto

Found 8 papers, 7 papers with code

Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding

1 code implementation12 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

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

1 code implementation23 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.

Imitation Learning Reinforcement Learning (RL)

Bootstrapped Training of Score-Conditioned Generator for Offline Design of Biological Sequences

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.

Meta-SysId: A Meta-Learning Approach for Simultaneous Identification and Prediction

no code implementations1 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.

Meta-Learning regression +3

DevFormer: A Symmetric Transformer for Context-Aware Device Placement

2 code implementations26 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.

Combinatorial Optimization Meta-Learning

Neural Solvers for Fast and Accurate Numerical Optimal Control

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.

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