5 citations · 15 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
Failure Modes of Maximum Entropy RLHF
Ömer Veysel Çağatan, Barış Akgün
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…
Uncovering RL Integration in SSL Loss: Objective-Specific Implications for Data-Efficient RL
Ömer Veysel Çağatan, Barış Akgün
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…
BarlowRL: Barlow Twins for Data-Efficient Reinforcement Learning
Omer Veysel Cagatan, Baris Akgun
This paper introduces BarlowRL, a data-efficient reinforcement learning agent that combines the Barlow Twins self-supervised learning framework with DER (Data-Efficient Rainbow) al…
FLAGS Framework for Comparative Analysis of Federated Learning Algorithms
Ahnaf Hannan Lodhi, Barış Akgün, Öznur Özkasap
Federated Learning (FL) has become a key choice for distributed machine learning. Initially focused on centralized aggregation, recent works in FL have emphasized greater decentral…
Flow From Motion: A Deep Learning Approach
Cem Eteke, Hayati Havlucu, Nisa İrem Kırbaç +5
Wearable devices have the potential to enhance sports performance, yet they are not fulfilling this promise. Our previous studies with 6 professional tennis coaches and 20 players…