DARL is a two-stage MARL technique that trains robust evasion policies for a single satellite evader against multiple adversarial pursuers in a partially observable orbital game, outperforming standard optimization planners.
Fixed-Time Zero-Sum Pursuit–Evasion Game Control of Multisatellite via Adaptive Dynamic Programming
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Satellite Chasers: Divergent Adversarial Reinforcement Learning to Engage Intelligent Adversaries on Orbit
DARL is a two-stage MARL technique that trains robust evasion policies for a single satellite evader against multiple adversarial pursuers in a partially observable orbital game, outperforming standard optimization planners.