arXiv AI
Risk-Sensitive Mission Planning for Multi-Target On-Orbit Inspection via Distributional Reinforcement Learning
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
5d ago
Chance-Constrained Inspection Viewpoint Sequencing for On-Orbit Servicing via Safe Reinforcement Learning
arXiv:2610.41639v1 Announce Type: new Abstract: Autonomous inspection of a target spacecraft is typically the initial step in on-orbit servicing. Planning close-proximity observation viewpoints invol...
arXiv AI
Sep 10
Mini-Batch Risk-Averse Deep Q-Learning: A Robot Navigation Case Study
arXiv:2609.07998v1 Announce Type: new Abstract: We study the control of Markov decision processes in which the quality of a policy is evaluated by a dynamic, time-consistent Markov risk measure rathe...
arXiv Machine Learning
Oct 22
Brittle by Design? Multi-Scenario Reinforcement Learning for Robust Mission Coordination in Systems of Systems
arXiv:2610.28818v1 Announce Type: cross Abstract: As systems engineering (SE) shifts from monolithic systems to Systems of Systems, Mission Engineering (ME) has emerged as a new line of thinking in S...
arXiv AI
Oct 31
Where Planning Changes the Outcome of Orbital Games
The paper introduces OrbitalGym, a JAX‑based simulation environment for studying how planning algorithms and system design influence the outcomes of adversarial orbital encounters. It supports multiple agents, diverse state representations, communication modes, and sensor models, and compiles games to batched GPU code for large‑scale parallel simulation. Using OrbitalGym, the authors evaluate several control policies (PD, LQR, MPC, MCTS) across two close‑range games—pursuit‑evasion and lady‑bandit‑guard—showing that propellant availability often dominates in pursuit‑evasion, while policy choice significantly improves defense in the lady‑bandit‑guard scenario.
arXiv AI
Jun 29
RS-Diffuser: Risk-Sensitive Diffusion Planning with Distributional Value Guidance
arXiv:2606. 27766v1 Announce Type: cross Abstract: Offline reinforcement learning enables policy learning from fixed datasets without additional environment interaction, making it appealing for safety-critical applications where online exploration is costly or unsafe.
arXiv AI
Aug 26
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.