activity
20202022
most citedA Convex Programming Approach to Data-Driven Risk-Averse Reinforcement Learning

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

collaborators

5 papers

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…

cs.AI2021

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…

eess.SY20214 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…