no code implementations • 27 Dec 2023 • Gulay Goktas Sever, Umut Demir, Abdullah Sadik Satir, Mustafa Cagatay Sahin, Nazim Kemal Ure
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 aircraft model parameters.
no code implementations • 6 Mar 2023 • Ahmet Semih Tasbas, Safa Onur Sahin, Nazim Kemal Ure
By this way, the training agent performs air combat simulations to an enemy with smarter strategies, which improves the performance and robustness of the agents.
1 code implementation • 28 Feb 2023 • Bengisu Guresti, Abdullah Vanlioglu, Nazim Kemal Ure
In order to resolve this issue, we propose the Incentive Q-Flow (IQ-Flow) algorithm, which modifies the system's reward setup with an incentive regulator agent such that the cooperative policy also corresponds to the self-interested policy for the agents.
no code implementations • 6 Dec 2022 • Umut Demir, A. Sadik Satir, Gulay Goktas Sever, Cansu Yikilmaz, Nazim Kemal Ure
Development of guidance, navigation and control frameworks/algorithms for swarms attracted significant attention in recent years.
2 code implementations • 29 Oct 2022 • Resul Dagdanov, Halil Durmus, Nazim Kemal Ure
In this work, we propose a self-improving artificial intelligence system to enhance the safety performance of reinforcement learning (RL)-based autonomous driving (AD) agents using black-box verification methods.
2 code implementations • 29 Oct 2022 • Resul Dagdanov, Feyza Eksen, Halil Durmus, Ferhat Yurdakul, Nazim Kemal Ure
In this paper, we present a Reinforcement Learning (RL) based methodology to DEtect and FIX (DeFIX) failures of an Imitation Learning (IL) agent by extracting infraction spots and re-constructing mini-scenarios on these infraction areas to train an RL agent for fixing the shortcomings of the IL approach.
no code implementations • 15 Oct 2022 • Umut Demir, Nazim Kemal Ure
In this work we propose a reinforcement learning (RL) framework that controls the density of a large-scale swarm for engaging with adversarial swarm attacks.
no code implementations • 28 Sep 2022 • Mehmetcan Kaymaz, Nazim Kemal Ure
We compare our algorithm with other clustering methods and show that when coupled with a trajectory planner, the overall system can efficiently traverse unknown environments in the presence of dynamic obstacles.
no code implementations • 28 Nov 2021 • Bengisu Guresti, Nazim Kemal Ure
Centralization and decentralization are two approaches used for cooperation in MARL.
no code implementations • 14 Mar 2021 • Anil Ozturk, Mustafa Burak Gunel, Resul Dagdanov, Mirac Ekim Vural, Ferhat Yurdakul, Melih Dal, Nazim Kemal Ure
The main contribution of this paper is a systematic study for investigating the value of curriculum reinforcement learning in autonomous driving applications.
no code implementations • 26 Feb 2021 • Reyhan Kevser Keser, Aydin Ayanzadeh, Omid Abdollahi Aghdam, Caglar Kilcioglu, Behcet Ugur Toreyin, Nazim Kemal Ure
One of the most efficient methods for model compression is hint distillation, where the student model is injected with information (hints) from several different layers of the teacher model.
no code implementations • 4 Dec 2020 • Samet Uzun, Nazim Kemal Ure
This paper introduces a decentralized state-dependent Markov chain synthesis (DSMC) algorithm for finite-state Markov chains.
Optimization and Control Multiagent Systems Dynamical Systems Probability
no code implementations • 29 Jul 2020 • Yunus Bicer, Ali Alizadeh, Nazim Kemal Ure, Ahmetcan Erdogan, Orkun Kizilirmak
The objective of this paper is to develop a sample efficient end-to-end deep learning method for self-driving cars, where we attempt to increase the value of the information extracted from samples, through careful analysis obtained from each call to expert driver\'s policy.
no code implementations • 10 Jun 2020 • Anil Ozturk, Mustafa Burak Gunel, Melih Dal, Ugur Yavas, Nazim Kemal Ure
Automated lane changing is a critical feature for advanced autonomous driving systems.
no code implementations • 18 Sep 2019 • Ali Alizadeh, Majid Moghadam, Yunus Bicer, Nazim Kemal Ure, Ugur Yavas, Can Kurtulus
Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the trade-off between safety and agility.