Compliance with maritime traffic rules is essential for the safe operation of autonomous vessels, yet training reinforcement learning (RL) agents to adhere to them is challenging. The behavior of RL agents is shaped by the training scenarios they encounter, but creating scenarios that capture the complexity of maritime navigation is non-trivial, and real-world data alone is insufficient. To address this, we propose a falsification-driven RL approach that generates adversarial training scenarios in which the vessel under test violates maritime traffic rules, which are expressed as signal temporal logic specifications. Our experiments on open-sea navigation with two vessels demonstrate that the proposed approach provides more relevant training scenarios and achieves more consistent rule compliance.

We consider a moving target that we seek to learn from samples. Our results extend randomized techniques developed in control and optimization for a constant target to the case where the target is changing. We derive a novel bound on the number of samples that are required to construct a probably approximately correct (PAC) estimate of the target. Furthermore, when the moving target is a convex polytope, we provide a constructive method of generating the PAC estimate using a mixed integer linear program (MILP). The proposed method is demonstrated on an application to autonomous emergency braking.

Machine learning algorithms have exciting and wide-range potential. However, as the data frequently containsensitive personal information, there are real privacy concerns associated with the development and the deployment of this technology. Motivated by this observation, the line of work on differentially private learning (initiated by [23]) aims to construct learning algorithms that provide strong (mathematically proven) privacy protections for the training data. Both government agenciesand industrial companies have realized the importance of introducing strong privacy protection to statistical and machine learning tasks. A few recent examples include Google [20] and Apple [27] that are already using differentially private estimation algorithms that feed into machine learning algorithms, and the US Census Bureau announcement that they will use differentially privatedata publication techniques in the next decennial census [1]. Differential privacy is increasingly accepted as a standard for rigorous privacy. We refer the reader to the excellent surveys in [17] and [28]. The definition of differential privacy is, Definition 1.1 ([16]). Let A be a randomized algorithm whose input is a sample.