SBSRL approximates worst-case safety optimization over uncertain dynamics via finite sampling, adds epistemic-uncertainty-constrained exploration, and supplies high-probability safety guarantees plus finite-time sample-complexity bounds for near-optimal policies.
Finite-sample-based reachability for safe control with gaussian process dynamics.arXiv preprint arXiv:2505.07594, 2025.(Cited on pages 2, 5, 15, 16, and 17)
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A robust adaptive MPC framework for nonlinear systems with bounded disturbances uses Gaussian process models and contraction metrics to guarantee recursive feasibility, robust constraint satisfaction, and convergence with high probability.
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Sampling-Based Safe Reinforcement Learning
SBSRL approximates worst-case safety optimization over uncertain dynamics via finite sampling, adds epistemic-uncertainty-constrained exploration, and supplies high-probability safety guarantees plus finite-time sample-complexity bounds for near-optimal policies.
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A robust and adaptive MPC formulation for Gaussian process models
A robust adaptive MPC framework for nonlinear systems with bounded disturbances uses Gaussian process models and contraction metrics to guarantee recursive feasibility, robust constraint satisfaction, and convergence with high probability.