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.
Safe exploration in finite markov decision processes with gaussian processes.International Conference on Neural Information Processing Systems, 2016.(Cited on pages 2 and 7)
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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.