4 citations · 4 across the 3 of their papers we have counts for
5 papers
Assured Learning-enabled Autonomy: A Metacognitive Reinforcement Learning Framework
Aquib Mustafa, Majid Mazouchi, Subramanya Nageshrao +1
Reinforcement learning (RL) agents with pre-specified reward functions cannot provide guaranteed safety across variety of circumstances that an uncertain system might encounter. To…
A Convex Programming Approach to Data-Driven Risk-Averse Reinforcement Learning
Yuzhen Han, Majid Mazouchi, Subramanya Nageshrao +1
This paper presents a model-free reinforcement learning (RL) algorithm to solve the risk-averse optimal control (RAOC) problem for discrete-time nonlinear systems. While successful…
An online evolving framework for advancing reinforcement-learning based automated vehicle control
Teawon Han, Subramanya Nageshrao, Dimitar P. Filev +1
In this paper, an online evolving framework is proposed to detect and revise a controller's imperfect decision-making in advance. The framework consists of three modules: the evolv…
Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous Vehicles
Songan Zhang, Huei Peng, Subramanya Nageshrao +1
Deep reinforcement learning methods have been widely used in recent years for autonomous vehicle's decision-making. A key issue is that deep neural networks can be fragile to adver…
Deep Reinforcement Learning with Enhanced Safety for Autonomous Highway Driving
Ali Baheri, Subramanya Nageshrao, H. Eric Tseng +3
In this paper, we present a safe deep reinforcement learning system for automated driving. The proposed framework leverages merits of both rule-based and learning-based approaches…