Sampling-Based Safe Reinforcement Learning
Sampling-Based Safe Reinforcement Learning (SBSRL) is a model-based RL method that ensures safety during learning by checking constraints across a small set of sampled dynamics models instead of solving an impossible worst-case problem. It uses epistemic uncertainty as a constraint to drive exploration, leading to high-probability safety guarantees and near-optimal policies in continuous control tasks.