Copertina

Towards Learning Foundation Models for Heuristic Functions to Solve Pathfinding Problems

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Khandelwal, Vedant, Sheth, Amit, Agostinelli, Forest
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

The DeepXube Software Package for Solving Pathfinding Problems with Learned Heuristic Functions and Search
di: Agostinelli, Forest
Pubblicazione: (2026)

PDDLFuse: A Tool for Generating Diverse Planning Domains
di: Khandelwal, Vedant, et al.
Pubblicazione: (2024)

A* Search Without Expansions: Learning Heuristic Functions with Deep Q-Networks
di: Agostinelli, Forest, et al.
Pubblicazione: (2021)

Deep Heuristic Learning for Real-Time Urban Pathfinding
di: El-Ela, Mohamed Hussein Abo, et al.
Pubblicazione: (2024)

Beyond Single-Step Updates: Reinforcement Learning of Heuristics with Limited-Horizon Search
di: Hadar, Gal, et al.
Pubblicazione: (2025)

Time Series Foundational Models: Their Role in Anomaly Detection and Prediction
di: Shyalika, Chathurangi, et al.
Pubblicazione: (2024)

HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding
di: Tang, Huijie, et al.
Pubblicazione: (2024)

Tree of Thoughts as a Classical Heuristic Search Problem: Formal Foundations and Design Patterns
di: Sharon, Guni
Pubblicazione: (2026)

Sequential Compression Layers for Efficient Federated Learning in Foundational Models
di: Mahla, Navyansh, et al.
Pubblicazione: (2024)

Solving Collaborative Dec-POMDPs with Deep Reinforcement Learning Heuristics
di: Soffair, Nitsan
Pubblicazione: (2022)

Deep Reinforcement Learning for Solving Management Problems: Towards A Large Management Mode
di: Jiang, Jinyang, et al.
Pubblicazione: (2024)

An Unsupervised Learning Framework Combined with Heuristics for the Maximum Minimal Cut Problem
di: Liu, Huaiyuan, et al.
Pubblicazione: (2024)

SCNO: Spiking Compositional Neural Operator -- Towards a Neuromorphic Foundation Model for Nuclear PDE Solving
di: Roy, Samrendra, et al.
Pubblicazione: (2026)

Unsupervised Learning for Solving the Travelling Salesman Problem
di: Min, Yimeng, et al.
Pubblicazione: (2023)

Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Models
di: Binkyte, Ruta, et al.
Pubblicazione: (2025)

Matching Problems to Solutions: An Explainable Way of Solving Machine Learning Problems
di: Saleh, Lokman, et al.
Pubblicazione: (2024)

Understanding Sample Generation Strategies for Learning Heuristic Functions in Classical Planning
di: Bettker, R. V., et al.
Pubblicazione: (2022)

Code-Guided Reasoning for Small Language Models: Evaluating Executable MCQA Scaffolds
di: Biswas, Prateek, et al.
Pubblicazione: (2026)

Solving Freshness in RAG: A Simple Recency Prior and the Limits of Heuristic Trend Detection
di: Grofsky, Matthew
Pubblicazione: (2025)

Enhancing Q-Learning with Large Language Model Heuristics
di: Wu, Xiefeng
Pubblicazione: (2024)

When, How Long and How Much? Interpretable Neural Networks for Time Series Regression by Learning to Mask and Aggregate
di: Forest, Florent, et al.
Pubblicazione: (2025)

Principled Data Augmentation for Learning to Solve Quadratic Programming Problems
di: Qian, Chendi, et al.
Pubblicazione: (2025)

Learning to Solve Orienteering Problem with Time Windows and Variable Profits
di: Gao, Songqun, et al.
Pubblicazione: (2026)

Toward Privileged Foundation Models:LUPI for Accelerated and Improved Learning
di: Ding, Xueying, et al.
Pubblicazione: (2026)

Towards a Law of Iterated Expectations for Heuristic Estimators
di: Christiano, Paul, et al.
Pubblicazione: (2024)

Adaptive Variational Continual Learning via Task-Heuristic Modelling
di: Yang, Fan
Pubblicazione: (2024)

Ten Challenging Problems in Federated Foundation Models
di: Fan, Tao, et al.
Pubblicazione: (2025)

PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning
di: Metelo, Frederico, et al.
Pubblicazione: (2024)

Learning Solution-Aware Transformers for Efficiently Solving Quadratic Assignment Problem
di: Tan, Zhentao, et al.
Pubblicazione: (2024)

LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch
di: Jiang, Caigao, et al.
Pubblicazione: (2024)

An End-to-End Learning Approach for Solving Capacitated Location-Routing Problems
di: Miao, Changhao, et al.
Pubblicazione: (2025)

An Overview and Discussion of the Suitability of Existing Speech Datasets to Train Machine Learning Models for Collective Problem Solving
di: Villuri, Gnaneswar, et al.
Pubblicazione: (2024)

Towards Physics-Guided Foundation Models
di: Farhadloo, Majid, et al.
Pubblicazione: (2025)

Towards a Physics Foundation Model
di: Wiesner, Florian, et al.
Pubblicazione: (2025)

Foundation Model Forecasts: Form and Function
di: Perez-Diaz, Alvaro, et al.
Pubblicazione: (2025)

Deep Networks Learn Deep Hierarchical Models
di: Daniely, Amit
Pubblicazione: (2026)

MAPF-GPT: Imitation Learning for Multi-Agent Pathfinding at Scale
di: Andreychuk, Anton, et al.
Pubblicazione: (2024)

Learning to Communicate Locally for Large-Scale Multi-Agent Pathfinding
di: Vyaltsev, Valeriy, et al.
Pubblicazione: (2026)

OR-Toolformer: Modeling and Solving Operations Research Problems with Tool Augmented Large Language Models
di: Zhang, Jianzhang, et al.
Pubblicazione: (2025)

Task Aware Modulation using Representation Learning: An Approach for Few Shot Learning in Environmental Systems
di: Renganathan, Arvind, et al.
Pubblicazione: (2023)