← Back to all news
arXiv AI By Alberto Luise, Michele Lombardi

Hybrid Reinforcement Learning and Search for Flight Trajectory Planning

Read the original on arXiv AI →

The paper investigates combining Reinforcement Learning (RL) with search-based path planners to accelerate flight trajectory optimization for airliners. An RL agent is trained to generate near‑optimal paths from location and atmospheric data, which then constrain a traditional solver to reduce its search space. Experiments using Airbus performance models show that fuel consumption deviates by less than 1% from an unconstrained solver while computation time improves by up to 50%.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv AI
2d ago

Reinforcement Learning Enhanced LLM Agents for Complex Vehicle Routing Problems

The paper introduces Reinforcement Learning Enhanced LLM Agents (RLEA), a multi‑agent framework that automates the modeling of complex Vehicle Routing Problems (VRPs). RLEA employs a lightweight neural Planner trained with Soft Q‑learning to coordinate LLM‑based agents, and incorporates an evolutionary memory module and retrieval‑augmented generation to leverage experience and external solver knowledge. Experiments on 48 VRP variants show that RLEA outperforms the prior state‑of‑the‑art method, achieving a 16.67% higher success rate and significantly reducing runtime errors.

By Yi Chen, Zikang Yu, Jiahai Wang, Jinbiao Chen, Jianpeng Zhou, Zizhen Zhang
llmsragagentsreinforcement-learning
More like this →
arXiv AI
Aug 18

LAPF: LLM-Agent-Based Path Finder Using the UAVScenes Dataset

arXiv:2608. 15175v1 Announce Type: cross Abstract: Uncrewed aerial vehicles (UAVs) are increasingly deployed for autonomous navigation in complex outdoor environments, where dynamic conditions and mission requirements require intelligent adaptive decision-making.

By Yousef Emami, Mohammadhossein Homaei, Hao Zhou, Miguel Guti\'errez Gait\'an, Atefeh Hajijamali Arani, Rui Zhang
llmsagentsreinforcement-learning
More like this →
arXiv AI
Aug 25

Memory-Enhanced Neural Solvers for Routing Problems

The paper introduces MEMENTO, a memory‑enhanced neural solver that improves routing problem solutions by using online data from repeated attempts to adjust action distributions during inference. It targets NP‑hard routing tasks such as the Traveling Salesman and Capacitated Vehicle Routing problems, outperforming existing tree‑search and policy‑gradient fine‑tuning methods. MEMENTO demonstrates strong scalability and data efficiency, achieving state‑of‑the‑art results on 11 of 12 evaluated tasks and enabling zero‑shot integration with diversity‑based solvers.

By Felix Chalumeau, Refiloe Shabe, Noah De Nicola, Arnu Pretorius, Thomas D. Barrett, Nathan Grinsztajn
reinforcement-learningfine-tuningbenchmarks
More like this →
arXiv AI
Aug 14

Exploiting Symbolic Heuristics for the Synthesis of Domain-Specific Temporal Planning Guidance using Reinforcement Learning

arXiv:2505. 13372v2 Announce Type: replace Abstract: Recent work investigated the use of Reinforcement Learning (RL) for the synthesis of heuristic guidance to improve the performance of temporal planners when a domain is fixed and a set of training problems (not plans) is given.

By Irene Brugnara, Alessandro Valentini, Andrea Micheli
reinforcement-learning
More like this →