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Probabilistic Counterexample Guidance for Safer Reinforcement Learning (Extended Version)
We demonstrate our method's effectiveness in reducing safety violations during online exploration in preliminary experiments by an average of 40. 3% compared with QL and DQN standard algorithms and 29. 1% compared with previous related work, while achieving comparable cumulative rewards with respect to unrestricted exploration and alternative approaches.
Revealing Reliable Signatures by Learning Top-Rank Pairs
Signature verification, as a crucial practical documentation analysis task, has been continuously studied by researchers in machine learning and pattern recognition fields.
Optimal Rejection Function Meets Character Recognition Tasks
The highlights of LwR are: (1) the rejection strategy is not heuristic but has a strong background from a machine learning theory, and (2) the rejection function can be trained on an arbitrary feature space which is different from the feature space for classification.
