Benchmarking Safe Deep Reinforcement Learning in Aquatic Navigation
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.
Code (0)
등록된 구현이 없습니다.
Tasks
BenchmarkingDeep Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Safe Reinforcement LearningSimilar Papers 제목 키워드 기반
Aquatic Navigation: A Challenging Benchmark for Deep Reinforcement Learning
An exciting and promising frontier for Deep Reinforcement Learning (DRL) is its application to real-world robotic systems. While modern DRL approaches achieved remarkable successes in many robotic scenarios (including mo…
Autonomous DrivingBenchmarkingDeep Reinforcement Learningreinforcement-learning+1Designing Control Barrier Function via Probabilistic Enumeration for Safe Reinforcement Learning Navigation
Achieving safe autonomous navigation systems is critical for deploying robots in dynamic and uncertain real-world environments. In this paper, we propose a hierarchical control framework leveraging neural network verific…
Autonomous NavigationSafe Reinforcement LearningDepth-Constrained ASV Navigation with Deep RL and Limited Sensing
Autonomous Surface Vehicles (ASVs) play a crucial role in maritime operations, yet their navigation in shallow-water environments remains challenging due to dynamic disturbances and depth constraints. Traditional navigat…
Decision MakingReinforcement Learning (RL)Adversarial Reinforcement Learning Framework for Benchmarking Collision Avoidance Mechanisms in Autonomous Vehicles
With the rapidly growing interest in autonomous navigation, the body of research on motion planning and collision avoidance techniques has enjoyed an accelerating rate of novel proposals and developments. However, the co…
Autonomous NavigationAutonomous VehiclesBenchmarkingCollision Avoidance+5NavBench: A Unified Robotics Benchmark for Reinforcement Learning-Based Autonomous Navigation
Autonomous robots must navigate and operate in diverse environments, from terrestrial and aquatic settings to aerial and space domains. While Reinforcement Learning (RL) has shown promise in training policies for specifi…
Autonomous NavigationBenchmarkingNavigateReinforcement Learning (RL)