13 citations · 28 across the 9 of their papers we have counts for
5 papers · 1 filter
A Scalable Reinforcement Learning Approach for Attack Allocation in Swarm to Swarm Engagement Problems
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. Although there is a si…
Learning How to Trade-Off Safety with Agility Using Deep Covariance Estimation for Perception Driven UAV Motion Planning
Onur Akgun, Kamil Canberk Atik, Mustafa Erdem +3
We investigate how to utilize predictive models for selecting appropriate motion planning strategies based on perception uncertainty estimation for agile unmanned aerial vehicle (U…
Sample Efficient Interactive End-to-End Deep Learning for Self-Driving Cars with Selective Multi-Class Safe Dataset Aggregation
Yunus Bicer, Ali Alizadeh, Nazim Kemal Ure +2
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 extrac…
Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents
Anil Ozturk, Mustafa Burak Gunel, Melih Dal +2
Automated lane changing is a critical feature for advanced autonomous driving systems. In recent years, reinforcement learning (RL) algorithms trained on traffic simulators yielded…
Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment
Ali Alizadeh, Majid Moghadam, Yunus Bicer +3
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 tra…