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

arXiv:2608.24471 · cs.AI · Submitted 2026-08-25 · Read on arXiv

cs.AI

Submitted: 2026-08-25

Updated: 2026-08-25

Comments: 26 pages, 12 figures

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

Q-learning — 10× in this paper · explained in 3 episodes
Implicit Q-learning — 9× in this paper
Reinforcement Learning — 9× in this paper · explained in 4 episodes
Combinatorial Optimization — 4× in this paper · explained in 2 episodes
Objective Function — 4× in this paper · explained in 2 episodes
Action Space — 2× in this paper · explained in 1 episode
Adaptation — 2× in this paper · explained in 1 episode
Deep Reinforcement Learning — 2× in this paper · explained in 2 episodes
Generalization — 2× in this paper · explained in 13 episodes
Resource Constraints — 2× in this paper · explained in 3 episodes
Adam — 1× in this paper · explained in 2 episodes
Aggregation — 1× in this paper · explained in 1 episode
Consistency — 1× in this paper · explained in 3 episodes
Core — 1× in this paper · explained in 2 episodes
Distribution Shift — 1× in this paper · explained in 5 episodes
Energy Efficiency — 1× in this paper · explained in 3 episodes
Loss Function — 1× in this paper · explained in 2 episodes
Online Adaptation — 1× in this paper · explained in 3 episodes
Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
Reward Function — 1× in this paper · explained in 2 episodes
Robustness — 1× in this paper · explained in 5 episodes
Transfer Learning — 1× in this paper · explained in 10 episodes
Transformer — 1× in this paper · explained in 2 episodes

Sources

Earth Observation Satellite Scheduling with Graph Neural Networks and Monte Carlo Tree Search

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