水上ナビゲーションにおける安全な深層強化学習のベンチマーク
Benchmarking Safe Deep Reinforcement Learning in Aquatic Navigation
水上ロボットの安全な航法のための新しいベンチマーク環境を提案し、勾配ベースと非勾配ベースを組み合わせた学習法と区間解析による検証手法を評価した論文。
著者: Enrico Marchesini, Davide Corsi, Alessandro Farinelli
分類: cs.LG, cs.AI, cs.RO
原文アブストラクト
We propose a novel benchmark environment for Safe Reinforcement Learning focusing on aquatic navigation. Aquatic navigation is an extremely challenging task due to the non-stationary environment and the uncertainties of the robotic platform, hence it is crucial to consider the safety aspect of the problem, by analyzing the behavior of the trained network to avoid dangerous situations (e.g., collisions). To this end, we consider a value-based and policy-gradient Deep Reinforcement Learning (DRL) and we propose a crossover-based strategy that combines gradient-based and gradient-free DRL to improve sample-efficiency. Moreover, we propose a verification strategy based on interval analysis that checks the behavior of the trained models over a set of desired properties. Our results show that the crossover-based training outperforms prior DRL approaches, while our verification allows us to quantify the number of configurations that violate the behaviors that are described by the properties. Crucially, this will serve as a benchmark for future research in this domain of applications.
