5 papers
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…
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…
Multi-Objective Memory Bandwidth Regulation and Cache Partitioning for Multicore Real-Time Systems
Binqi Sun, Zhihang Wei, Andrea Bastoni +5
Memory bandwidth regulation and cache partitioning are widely used techniques for achieving predictable timing in real-time computing systems. Combined with partitioned scheduling,…
Continuous World Coverage Path Planning for Fixed-Wing UAVs using Deep Reinforcement Learning
Mirco Theile, Andres R. Zapata Rodriguez, Marco Caccamo +1
Unmanned Aerial Vehicle (UAV) Coverage Path Planning (CPP) is critical for applications such as precision agriculture and search and rescue. While traditional methods rely on discr…
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…