63 citations · 96 across the 13 of their papers we have counts for
14 papers
Hierarchical Game-Based Multi-Agent Decision-Making for Autonomous Vehicles
Mushuang Liu, Yan Wan, Frank Lewis +3
This paper develops a game-theoretic decision-making framework for autonomous driving in multi-agent scenarios. A novel hierarchical game-based decision framework is developed for…
Faster Model Predictive Control via Self-Supervised Initialization Learning
Zhaoxin Li, Xiaoke Wang, Letian Chen +3
Model Predictive Control (MPC) is widely used in robot control by optimizing a sequence of control outputs over a finite-horizon. Computational approaches for MPC include determini…
Interpretable Reinforcement Learning for Robotics and Continuous Control
Rohan Paleja, Letian Chen, Yaru Niu +10
Interpretability in machine learning is critical for the safe deployment of learned policies across legally-regulated and safety-critical domains. While gradient-based approaches i…
Robust AI Driving Strategy for Autonomous Vehicles
Subramanya Nageshrao, Yousaf Rahman, Vladimir Ivanovic +4
There has been significant progress in sensing, perception, and localization for automated driving, However, due to the wide spectrum of traffic/road structure scenarios and the lo…
A Risk-Averse Preview-based -Learning Algorithm: Application to Highway Driving of Autonomous Vehicles
Majid Mazouchi, Subramanya Nageshrao, Hamidreza Modares
A risk-averse preview-based -learning planner is presented for navigation of autonomous vehicles. To this end, the multi-lane road ahead of a vehicle is represented by a finite-…
Finite-time Koopman Identifier: A Unified Batch-online Learning Framework for Joint Learning of Koopman Structure and Parameters
Majid Mazouchi, Subramanya Nageshrao, Hamidreza Modares
In this paper, a unified batch-online learning approach is introduced to learn a linear representation of nonlinear system dynamics using the Koopman operator. The presented system…
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…
Towards Interpretable-AI Policies Induction using Evolutionary Nonlinear Decision Trees for Discrete Action Systems
Yashesh Dhebar, Kalyanmoy Deb, Subramanya Nageshrao +2
Black-box AI induction methods such as deep reinforcement learning (DRL) are increasingly being used to find optimal policies for a given control task. Although policies represente…
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…
Autonomous Highway Driving using Deep Reinforcement Learning
Subramanya Nageshrao, Eric Tseng, Dimitar Filev
The operational space of an autonomous vehicle (AV) can be diverse and vary significantly. This may lead to a scenario that was not postulated in the design phase. Due to this, for…
Reinforcement learning for port-Hamiltonian systems
Olivier Sprangers, Gabriel A. D. Lopes, Robert Babuska
Passivity-based control (PBC) for port-Hamiltonian systems provides an intuitive way of achieving stabilization by rendering a system passive with respect to a desired storage func…