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05/27/2023
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities
We investigate safe multi-agent reinforcement learning, where agents see...
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05/27/2023
No-Regret Learning in Dynamic Competition with Reference Effects Under Logit Demand
This work is dedicated to the algorithm design in a competitive framewor...
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05/17/2023
Exact Recovery for System Identification with More Corrupt Data than Clean Data
In this paper, we study the system identification problem for linear dis...
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02/15/2023
Scalable Multi-Agent Reinforcement Learning with General Utilities
We study the scalable multi-agent reinforcement learning (MARL) with gen...
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02/15/2023
Over-parametrization via Lifting for Low-rank Matrix Sensing: Conversion of Spurious Solutions to Strict Saddle Points
This paper studies the role of over-parametrization in solving non-conve...
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11/19/2022
Non-stationary Risk-sensitive Reinforcement Learning: Near-optimal Dynamic Regret, Adaptive Detection, and Separation Design
We study risk-sensitive reinforcement learning (RL) based on an entropic...
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10/04/2022
Learning of Dynamical Systems under Adversarial Attacks – Null Space Property Perspective
We study the identification of a linear time-invariant dynamical system ...
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08/15/2022
Semidefinite Programming versus Burer-Monteiro Factorization for Matrix Sensing
Many fundamental low-rank optimization problems, such as matrix completi...
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05/22/2022
Policy-based Primal-Dual Methods for Convex Constrained Markov Decision Processes
We study convex Constrained Markov Decision Processes (CMDPs) in which t...
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01/28/2022
Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints
We consider primal-dual-based reinforcement learning (RL) in episodic co...
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10/19/2021
Factorization Approach for Low-complexity Matrix Completion Problems: Exponential Number of Spurious Solutions and Failure of Gradient Methods
It is well-known that the Burer-Monteiro (B-M) factorization approach ca...
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10/19/2021
Beyond Exact Gradients: Convergence of Stochastic Soft-Max Policy Gradient Methods with Entropy Regularization
Entropy regularization is an efficient technique for encouraging explora...
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10/19/2021
On the Global Convergence of Momentum-based Policy Gradient
Policy gradient (PG) methods are popular and efficient for large-scale r...
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10/17/2021
A Dual Approach to Constrained Markov Decision Processes with Entropy Regularization
We study entropy-regularized constrained Markov decision processes (CMDP...
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05/18/2021
Sharp Restricted Isometry Property Bounds for Low-rank Matrix Recovery Problems with Corrupted Measurements
In this paper, we study a general low-rank matrix recovery problem with ...
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05/31/2020
Global Convergence of MAML for LQR
The paper studies the performance of the Model-Agnostic Meta-Learning (M...
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08/04/2019
Boundary Defense against Cyber Threat for Power System Operation
The operation of power grids is becoming increasingly data-centric. Whil...
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01/07/2019
Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery
Nonconvex matrix recovery is known to contain no spurious local minima u...
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10/26/2018
Stability-certified reinforcement learning: A control-theoretic perspective
We investigate the important problem of certifying stability of reinforc...
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05/25/2018