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.
T−1X k=t+1 g(xk, π(xk)) # We can directly apply Corollary 2 of Sukhija et al. [62] to obtain Jg(π, f ′)−J g(π, f) =E τ f π
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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.