Showing cs.LGShow all
3 papers · 1 filter
cs.LG2026
Higher Resolution, Better Generalization: Unlocking Visual Scaling in Deep Reinforcement Learning
Raphael Trumpp, Ãmer Veysel ÃaÄatan, Ömer Veysel Çağatan +3
Pixel-based deep reinforcement learning agents are typically trained on heavily downsampled visual observations, a convention inherited from early benchmarks rather than grounded i…
cs.LG2025
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…
cs.LG2024
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…