Explainable Causal Reinforcement Learning for satellite anomaly response operations with zero-trust governance guarantees

Explainable Causal Reinforcement Learning for satellite anomaly response operations with zero-trust governance guarantees Introduction: A Signal in the Noise Last year, while deep in a late-night experiment with a multi-agent reinforcement learning (RL) system, I hit a wall that fundamentally changed how I think about autonomous decision-making. I had built a small constellation simulator—twelve virtual satellites, a ground station, and a policy network trained to respond to telemetry anomalies. The agent worked beautifully in simulation. It rerouted power, reconfigured payloads, and recovered