3 citations · 5 across the 6 of their papers we have counts for
6 papers
Iterative Active-Inactive Obstacle Classification for Time-Optimal Collision Avoidance
Mehmetcan Kaymaz, Nazim Kemal Ure
Time-optimal obstacle avoidance is a prevalent problem encountered in various fields, including robotics and autonomous vehicles, where the task involves determining a path for a m…
An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Data
Gulay Goktas Sever, Umut Demir, Abdullah Sadik Satir +2
In this paper, we present a methodology for constructing data-driven maneuver generation models for agile aircraft that can generalize across a wide range of trim conditions and ai…
Beyond Traditional DoE: Deep Reinforcement Learning for Optimizing Experiments in Model Identification of Battery Dynamics
Gokhan Budan, Francesca Damiani, Can Kurtulus +1
Model identification of battery dynamics is a central problem in energy research; many energy management systems and design processes rely on accurate battery models for efficiency…
Reinforcement Learning Based Self-play and State Stacking Techniques for Noisy Air Combat Environment
Ahmet Semih Tasbas, Safa Onur Sahin, Nazim Kemal Ure
Reinforcement learning (RL) has recently proven itself as a powerful instrument for solving complex problems and even surpassed human performance in several challenging application…
IQ-Flow: Mechanism Design for Inducing Cooperative Behavior to Self-Interested Agents in Sequential Social Dilemmas
Bengisu Guresti, Abdullah Vanlioglu, Nazim Kemal Ure
Achieving and maintaining cooperation between agents to accomplish a common objective is one of the central goals of Multi-Agent Reinforcement Learning (MARL). Nevertheless in many…
GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning
Doğay Kamar, Nazım Kemal Üre, Gözde Ünal
In this study, we address the problem of efficient exploration in reinforcement learning. Most common exploration approaches depend on random action selection, however these approa…