Distribution-Agnostic Robust Trajectory Optimization via Chance-Constrained Reinforcement Learning
This paper presents a distribution-agnostic robust trajectory-optimization framework based on chance-constrained reinforcement learning. The uncertainty is represented here through initial conditions and process noise, with the only requirement being that it can be sampled. A deterministic nominal trajectory is first computed offline, and reinforcement learning is then used only to robustify that baseline through a structured affine closed-loop correction law comprising a feedforward control adjustment and time-varying feedback gains. Probabilistic feasibility is enforced empirically through rollout-based upper-tail quantiles, while terminal dispersion is regulated through covariance-feas...
| Authors | Yashdeep Chaudhary, Roberto Armellin, Harry Holt et al. |
|---|---|
| Primary category | math.OC |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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