Human-Aligned Decision Transformers for satellite anomaly response operations for extreme data sparsity scenarios
The Moment the Satellite Went Silent
It was 3:47 AM on a Tuesday when the telemetry stream from the GEO-7 communications satellite dropped to zero. I was testing a reinforcement learning agent I'd been developing for autonomous satellite operations, and I watched in real-time as my carefully trained policy—one that had achieved 98.7% accuracy in simulated anomaly scenarios—froze. It had never seen a complete telemetry blackout. The training data contained gaps, sure, but nothing like this. The satellite was dead to the ground station, and my agent had no idea what to do.
That night, sitting in the glow of my monitor with a cold cup of coffee, I realized something fundamental about the problem I was trying to solve. We were approaching satellite anomaly response as a traditional sequential decision-making problem, but the reality was far more nuanced. Satellites in extreme environments don't just have missing data—they have radically incomplete data, contradictory signals, and scenarios where the cost of a wrong decision is measured in billions of dollars and years of lost mission time.
