Satellite Chasers: Divergent Adversarial Reinforcement Learning to Engage Intelligent Adversaries on Orbit
This work addresses the need for robust autonomous evasion strategies in contested space environments, a domain where current methods are insufficient.
The paper introduces Divergent Adversarial Reinforcement Learning (DARL) to train robust evasion strategies for satellites pursued by multiple adversarial spacecraft. In a cat-and-mouse satellite scenario, DARL outperforms optimization-based path planners, producing highly robust models for adversarial multi-agent space environments.
As space becomes increasingly crowded and contested, robust autonomous capabilities for multi-agent environments are gaining critical importance. Current autonomous systems in space primarily rely on optimization-based path planning or long-range orbital maneuvers, which have not yet proven effective in adversarial scenarios where one satellite is actively pursuing another. We introduce Divergent Adversarial Reinforcement Learning (DARL), a two-stage Multi-Agent Reinforcement Learning (MARL) approach designed to train autonomous evasion strategies for satellites engaged with multiple adversarial spacecraft. Our method enhances exploration during training by promoting diverse adversarial strategies, leading to more robust and adaptable evader models. We validate DARL through a cat-and-mouse satellite scenario, modeled as a partially observable multi-agent capture the flag game where two adversarial ``cat" spacecraft pursue a single ``mouse" evader. DARL's performance is compared against several benchmarks, including an optimization-based satellite path planner, demonstrating its ability to produce highly robust models for adversarial multi-agent space environments.