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20182022
most citedA Convex Programming Approach to Data-Driven Risk-Averse Reinforcement Learning

4 citations · 4 across the 5 of their papers we have counts for

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8 papers · 1 filter

eess.SY2022

A Convex Optimization Approach for Control of Linear Quadratic Systems with Multiplicative Noise via System Level Synthesis

Majid Mazouchi, Farzaneh Tatari, Hamidreza Modares

This paper presents a convex optimization-based solution to the design of state-feedback controllers for solving the linear quadratic regulator (LQR) problem of uncertain discrete-…

eess.SY2022

Performance Analysis of Event-Triggered Consensus Control for Multi-agent Systems under Cyber-Physical Attacks

Farzaneh Tatari, Aquib Mustafa, Majid Mazouchi +3

This work presents a rigorous analysis of the adverse effects of cyber-physical attacks on the performance of multi-agent consensus with event-triggered control protocols. It is sh…

eess.SY2021

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-…

eess.SY2021

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…

eess.SY2021★ 4 cited

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…

eess.SY2020

Data-driven Dynamic Multi-objective Optimal Control: An Aspiration-satisfying Reinforcement Learning Approach

Majid Mazouchi, Yongliang Yang, Hamidreza Modares

This paper presents an iterative data-driven algorithm for solving dynamic multi-objective (MO) optimal control problems arising in control of nonlinear continuous-time systems. It…