4 papers
2-Level Reinforcement Learning for Ships on Inland Waterways: Path Planning and Following
Martin Waltz, Niklas Paulig, Ostap Okhrin
This paper proposes a realistic modularized framework for controlling autonomous surface vehicles (ASVs) on inland waterways (IWs) based on deep reinforcement learning (DRL). The f…
Two-step dynamic obstacle avoidance
Fabian Hart, Martin Waltz, Ostap Okhrin
Dynamic obstacle avoidance (DOA) is a fundamental challenge for any autonomous vehicle, independent of whether it operates in sea, air, or land. This paper proposes a two-step arch…
Addressing Maximization Bias in Reinforcement Learning with Two-Sample Testing
Martin Waltz, Ostap Okhrin
Value-based reinforcement-learning algorithms have shown strong results in games, robotics, and other real-world applications. Overestimation bias is a known threat to those algori…
Self-organized free-flight arrival for urban air mobility
Martin Waltz, Ostap Okhrin, Michael Schultz
Urban air mobility is an innovative mode of transportation in which electric vertical takeoff and landing (eVTOL) vehicles operate between nodes called vertiports. We outline a sel…