47 citations · 157 across the 29 of their papers we have counts for
6 papers · 1 filter
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…
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…
Vision-based DRL Autonomous Driving Agent with Sim2Real Transfer
Dianzhao Li, Ostap Okhrin
To achieve fully autonomous driving, vehicles must be capable of continuously performing various driving tasks, including lane keeping and car following, both of which are fundamen…
A Platform-Agnostic Deep Reinforcement Learning Framework for Effective Sim2Real Transfer towards Autonomous Driving
Dianzhao Li, Ostap Okhrin
Deep Reinforcement Learning (DRL) has shown remarkable success in solving complex tasks across various research fields. However, transferring DRL agents to the real world is still…
Robust Path Following on Rivers Using Bootstrapped Reinforcement Learning
Niklas Paulig, Ostap Okhrin
This paper develops a Deep Reinforcement Learning (DRL)-agent for navigation and control of autonomous surface vessels (ASV) on inland waterways. Spatial restrictions due to waterw…
Adaptive local VAR for dynamic economic policy uncertainty spillover
Niels Gillmann, Ostap Okhrin
The availability of data on economic uncertainty sparked a lot of interest in models that can timely quantify episodes of international spillovers of uncertainty. This challenging…