22 citations · 26 across the 10 of their papers we have counts for
11 papers
Higher Resolution, Better Generalization: Unlocking Visual Scaling in Deep Reinforcement Learning
Raphael Trumpp, Ömer Veysel Çağatan, Barış Akgün +1
Pixel-based deep reinforcement learning agents are typically trained on heavily downsampled visual observations, a convention inherited from early benchmarks rather than grounded i…
Efficient Real-World Autonomous Racing via Attenuated Residual Policy Optimization
Raphael Trumpp, Denis Hoornaert, Mirco Theile +1
Residual policy learning (RPL), in which a learned policy refines a static base policy using deep reinforcement learning (DRL), has shown strong performance across various robotic…
From Marginal to Joint Predictions: Evaluating Scene-Consistent Trajectory Prediction Approaches for Automated Driving
Fabian Konstantinidis, Ariel Dallari Guerreiro, Raphael Trumpp +4
Accurate motion prediction of surrounding traffic participants is crucial for the safe and efficient operation of automated vehicles in dynamic environments. Marginal prediction mo…
Impoola: The Power of Average Pooling for Image-Based Deep Reinforcement Learning
Raphael Trumpp, Ansgar Schäfftlein, Mirco Theile +1
As image-based deep reinforcement learning tackles more challenging tasks, increasing model size has become an important factor in improving performance. Recent studies achieved th…
Action Mapping for Reinforcement Learning in Continuous Environments with Constraints
Mirco Theile, Lukas Dirnberger, Raphael Trumpp +2
Deep reinforcement learning (DRL) has had success across various domains, but applying it to environments with constraints remains challenging due to poor sample efficiency and slo…
RaceMOP: Mapless Online Path Planning for Multi-Agent Autonomous Racing using Residual Policy Learning
Raphael Trumpp, Ehsan Javanmardi, Jin Nakazato +2
The interactive decision-making in multi-agent autonomous racing offers insights valuable beyond the domain of self-driving cars. Mapless online path planning is particularly of pr…
Unifying F1TENTH Autonomous Racing: Survey, Methods and Benchmarks
Benjamin David Evans, Raphael Trumpp, Marco Caccamo +4
The F1TENTH autonomous driving platform, consisting of 1:10-scale remote-controlled cars, has evolved into a well-established education and research platform. The many publications…
Efficient Learning of Urban Driving Policies Using Bird's-Eye-View State Representations
Raphael Trumpp, Martin Büchner, Abhinav Valada +1
Autonomous driving involves complex decision-making in highly interactive environments, requiring thoughtful negotiation with other traffic participants. While reinforcement learni…
Residual Policy Learning for Vehicle Control of Autonomous Racing Cars
Raphael Trumpp, Denis Hoornaert, Marco Caccamo
The development of vehicle controllers for autonomous racing is challenging because racing cars operate at their physical driving limit. Prompted by the demand for improved perform…
Learning to Generate All Feasible Actions
Mirco Theile, Daniele Bernardini, Raphael Trumpp +3
Modern cyber-physical systems are becoming increasingly complex to model, thus motivating data-driven techniques such as reinforcement learning (RL) to find appropriate control age…
Modeling Interactions of Autonomous Vehicles and Pedestrians with Deep Multi-Agent Reinforcement Learning for Collision Avoidance
Raphael Trumpp, Harald Bayerlein, David Gesbert
Reliable pedestrian crash avoidance mitigation (PCAM) systems are crucial components of safe autonomous vehicles (AVs). The nature of the vehicle-pedestrian interaction where decis…