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arXiv AI

Implicit Q-learning-bootstrapped ant colony optimization for maritime moving-target observation scheduling with agile satellites

The paper introduces IQACO, an implicit Q‑learning‑bootstrapped ant colony optimization algorithm for scheduling maritime moving‑target observations with agile Earth‑observation satellites. IQACO embeds an offline implicit Q‑learning module into the ant colony framework to adaptively adjust pheromone, heuristic, and evaporation parameters based on a compact search‑state representation. Experiments on 14 scenarios show IQACO outperforms conventional ant colony optimization, improving mean observation benefit by 3.40%–9.40%, accelerating convergence, and maintaining stability across different objective‑weight settings.

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Reinforcement Learning-Guided Evolutionary Policy Optimization for Preference-Adjustable Heterogeneous Agile Earth Observation Satellite Scheduling

The paper introduces a reinforcement‑learning‑guided evolutionary policy optimization framework for scheduling heterogeneous agile Earth observation satellites, addressing task selection, satellite assignment, and sequencing under diverse visibility windows, maneuvering constraints, energy use, and storage limits. It combines assignment‑based indirect encoding with decoder‑based cost evaluation to capture satellite‑dependent constraints while integrating task gain, energy savings, and load balance into a single utility metric. The resulting RLOSMEA algorithm uses reinforcement learning to select high‑level search operators, achieving higher weighted utility and more stable convergence than baseline metaheuristics across varied AEOS scenarios.

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arXiv AI
Jun 19

Oranits: Mission Assignment and Task Offloading in Open RAN-based ITS using Metaheuristic and Deep Reinforcement Learning

arXiv:2507. 19712v3 Announce Type: replace-cross Abstract: In this paper, we explore mission assignment and task offloading in an Open Radio Access Network (Open RAN)-based intelligent transportation system (ITS), where autonomous vehicles leverage mobile edge computing for efficient processing.

By Ngoc Hung Nguyen, Nguyen Van Thieu, Quang-Trung Luu, Anh Tuan Nguyen, Senura Wanasekara, Nguyen Cong Luong, Fatemeh Kavehmadavani, Van-Dinh Nguyen
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arXiv AI
Aug 20

Hybrid Reinforcement Learning and Search for Flight Trajectory Planning

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%.

By Alberto Luise, Michele Lombardi
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arXiv AI
Jun 11

Generalizing Beyond Suboptimality: Offline Reinforcement Learning Learns Effective Scheduling through Random Solutions

arXiv:2509. 10303v2 Announce Type: replace-cross Abstract: Online reinforcement learning (RL) approaches have demonstrated strong performance on Job Shop Scheduling (JSP) and Flexible JSP (FJSP) problems by learning scheduling policies through direct interaction with simulated environments.

By Jesse van Remmerden, Zaharah Bukhsh, Yingqian Zhang
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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
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arXiv AI
Jul 29

Distributed Constraint Optimization via Online Learning and Iterative Pricing with Application to Large-Scale Satellite Scheduling

arXiv:2607. 25835v1 Announce Type: new Abstract: Distributed constraint optimization problems (DCOPs) provide a popular framework for distributed decision making under limited communication, but many real-world instances are too large to solve monolithically.

By Itai Zilberstein, Pranav Rajbhandari, Steve Chien, Tuomas Sandholm
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