4 papers
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…
Failure Modes of Maximum Entropy RLHF
Ãmer Veysel ÃaÄatan, BarıŠAkgün
In this paper, we show that Simple Preference Optimization (SimPO) can be derived as Maximum Entropy Reinforcement Learning, providing a theoretical foundation for this reference-f…
Model-Based Adaptive Precision Control for Tabletop Planar Pushing Under Uncertain Dynamics
Aydin Ahmadi, Baris Akgun
Data-driven planar pushing methods have recently gained attention as they reduce manual engineering effort and improve generalization compared to analytical approaches. However, mo…
Uncovering RL Integration in SSL Loss: Objective-Specific Implications for Data-Efficient RL
Ãmer Veysel ÃaÄatan, BarıŠAkgün
In this study, we investigate the effect of SSL objective modifications within the SPR framework, focusing on specific adjustments such as terminal state masking and prioritized re…