You can use RL to add robustness to pre-computed trajectories by learning feedback corrections that work across different types of uncertainty—without needing to know the exact distribution in advance.
This paper combines reinforcement learning with chance constraints to make spacecraft trajectories robust to uncertainty without assuming a specific probability distribution. Starting from a baseline trajectory, the method learns feedback control adjustments that handle random variations in initial conditions and process noise, tested on Earth-Mars transfers and rocket landings.