47 citations · 157 across the 29 of their papers we have counts for
8 papers · 1 filter
Ethics-Aware Safe Reinforcement Learning for Rare-Event Risk Control in Interactive Urban Driving
Dianzhao Li, Ostap Okhrin
Autonomous vehicles hold great promise for reducing traffic fatalities and improving transportation efficiency, yet their widespread adoption hinges on embedding credible and trans…
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…
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…
Spatial-temporal recurrent reinforcement learning for autonomous ships
Martin Waltz, Ostap Okhrin
This paper proposes a spatial-temporal recurrent neural network architecture for deep -networks that can be used to steer an autonomous ship. The network design makes it possibl…
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…
Missing Velocity in Dynamic Obstacle Avoidance based on Deep Reinforcement Learning
Fabian Hart, Martin Waltz, Ostap Okhrin
We introduce a novel approach to dynamic obstacle avoidance based on Deep Reinforcement Learning by defining a traffic type independent environment with variable complexity. Fillin…
Formulation and validation of a car-following model based on deep reinforcement learning
Fabian Hart, Ostap Okhrin, Martin Treiber
We propose and validate a novel car following model based on deep reinforcement learning. Our model is trained to maximize externally given reward functions for the free and car-fo…