1 code implementation • 6 Mar 2024 • Wesley A. Suttle, Vipul K. Sharma, Krishna C. Kosaraju, S. Sivaranjani, Ji Liu, Vijay Gupta, Brian M. Sadler

We develop provably safe and convergent reinforcement learning (RL) algorithms for control of nonlinear dynamical systems, bridging the gap between the hard safety guarantees of control theory and the convergence guarantees of RL theory.

no code implementations • 2 Jan 2024 • Aritra Mitra, Lintao Ye, Vijay Gupta

Toward answering this question, we study a setting where a worker agent transmits quantized policy gradients (of the LQR cost) to a server over a noiseless channel with a finite bit-rate.

no code implementations • 16 Feb 2023 • Yihao Fang, Ilsang Ohn, Vijay Gupta, Lizhen Lin

We propose extrinsic and intrinsic deep neural network architectures as general frameworks for deep learning on manifolds.

no code implementations • 17 Oct 2022 • Lintao Ye, Ming Chi, Ruiquan Liao, Vijay Gupta

Under the assumption that the system is stable or given a known stabilizing controller, we show that our controller enjoys an expected regret that scales as $\sqrt{T}$ with the time horizon $T$ for the case of partially nested information pattern.

no code implementations • 25 Jul 2022 • Martin Figura, Yixuan Lin, Ji Liu, Vijay Gupta

In decentralized cooperative multi-agent reinforcement learning, agents can aggregate information from one another to learn policies that maximize a team-average objective function.

Multi-agent Reinforcement Learning Reinforcement Learning (RL)

1 code implementation • 25 Nov 2021 • Bernardo Aquino, Arash Rahnama, Peter Seiler, Lizhen Lin, Vijay Gupta

Adversarial examples can easily degrade the classification performance in neural networks.

no code implementations • NeurIPS 2021 • Yixuan Lin, Vijay Gupta, Ji Liu

While the convergence of consensus-based stochastic approximation algorithms when the interconnection among the agents is described by doubly stochastic matrices (at least in expectation) has been studied, less is known about the case when the interconnection matrix is simply stochastic.

1 code implementation • 12 Nov 2021 • Martin Figura, Yixuan Lin, Ji Liu, Vijay Gupta

We show that in the presence of Byzantine agents, whose estimation and communication strategies are completely arbitrary, the estimates of the cooperative agents converge to a bounded consensus value with probability one, provided that there are at most $H$ Byzantine agents in the neighborhood of each cooperative agent and the network is $(2H+1)$-robust.

Multi-agent Reinforcement Learning
reinforcement-learning
**+1**

no code implementations • 14 Oct 2021 • Lintao Ye, Hao Zhu, Vijay Gupta

We study the problem of control policy design for decentralized state-feedback linear quadratic control with a partially nested information structure, when the system model is unknown.

1 code implementation • Applied Optics 2021 • Bernardo Aquino, Stefano Castruccio, Vijay Gupta, Scott Howard

The problem of analyzing substances using low-cost sensors with a low signal-to-noise ratio (SNR) remains challenging.

no code implementations • 6 May 2021 • Nayara Aguiar, Parv Venkitasubramaniam, Vijay Gupta

For a duopoly in which agents are coupled in their payments, we show that if the principal and the agents interact finitely many times, the agents can derive rent by colluding even if the principal knows the types of the agents.

2 code implementations • 12 Mar 2021 • Soumyadip Ghosh, Bernardo Aquino, Vijay Gupta

To relieve some of this overhead, in this paper, we present EventGraD - an algorithm with event-triggered communication for stochastic gradient descent in parallel machine learning.

no code implementations • 11 Mar 2021 • Martin Figura, Krishna Chaitanya Kosaraju, Vijay Gupta

Recently, many cooperative distributed multi-agent reinforcement learning (MARL) algorithms have been proposed in the literature.

Multi-agent Reinforcement Learning
reinforcement-learning
**+1**

no code implementations • 25 Feb 2021 • Nayara Aguiar, Vijay Gupta, Rodrigo D. Trevizan, Babu R. Chalamala, Raymond H. Byrne

In a power distribution network with energy storage systems (ESS) and advanced controls, traditional monitoring and protection schemes are not well suited for detecting anomalies such as malfunction of controllable devices.

no code implementations • 22 Feb 2021 • Sayed Pouria Talebi, Stefan Werner, Vijay Gupta, Yih-Fang Huang

The paradigm of stability and convergence in distributed filtering is revised in this manuscript.

1 code implementation • 16 Jul 2020 • Yagiz Savas, Vijay Gupta, Ufuk Topcu

We model the agent's behavior as a Markov decision process, express its intrinsic motivation as a reward function, which belongs to a finite set of possible reward functions, and consider the incentives as additional rewards offered to the agent.

Optimization and Control

no code implementations • L4DC 2020 • S Sivaranjani, Etika Agarwal, Vijay Gupta

In model-based approaches to learning for controller design, it is important to first identify a system model from input-output data.

no code implementations • 13 Aug 2017 • Arash Rahnama, Abdullah Alchihabi, Vijay Gupta, Panos Antsaklis, Fatos T. Yarman Vural

We suggest a deep architecture which learns the natural groupings of the connectivity patterns of human brain in multiple time-resolutions.

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