184 citations · 354 across the 43 of their papers we have counts for
14 papers · 1 filter
A CMDP-within-online framework for Meta-Safe Reinforcement Learning
Vanshaj Khattar, Yuhao Ding, Bilgehan Sel +2
Meta-reinforcement learning has widely been used as a learning-to-learn framework to solve unseen tasks with limited experience. However, the aspect of constraint violations has no…
Tempo Adaptation in Non-stationary Reinforcement Learning
Hyunin Lee, Yuhao Ding, Jongmin Lee +3
We first raise and tackle a ``time synchronization'' issue between the agent and the environment in non-stationary reinforcement learning (RL), a crucial factor hindering its real-…
Scalable Primal-Dual Actor-Critic Method for Safe Multi-Agent RL with General Utilities
Donghao Ying, Yunkai Zhang, Yuhao Ding +2
We investigate safe multi-agent reinforcement learning, where agents seek to collectively maximize an aggregate sum of local objectives while satisfying their own safety constraint…
Exact Recovery for System Identification with More Corrupt Data than Clean Data
Baturalp Yalcin, Haixiang Zhang, Javad Lavaei +1
This paper investigates the system identification problem for linear discrete-time systems under adversaries and analyzes two lasso-type estimators. We examine both asymptotic and…
Scalable Multi-Agent Reinforcement Learning with General Utilities
Donghao Ying, Yuhao Ding, Alec Koppel +1
We study the scalable multi-agent reinforcement learning (MARL) with general utilities, defined as nonlinear functions of the team's long-term state-action occupancy measure. The o…
Non-stationary Risk-sensitive Reinforcement Learning: Near-optimal Dynamic Regret, Adaptive Detection, and Separation Design
Yuhao Ding, Ming Jin, Javad Lavaei
We study risk-sensitive reinforcement learning (RL) based on an entropic risk measure in episodic non-stationary Markov decision processes (MDPs). Both the reward functions and the…
Policy-based Primal-Dual Methods for Concave CMDP with Variance Reduction
Donghao Ying, Mengzi Amy Guo, Hyunin Lee +3
We study Concave Constrained Markov Decision Processes (Concave CMDPs) where both the objective and constraints are defined as concave functions of the state-action occupancy measu…
Provably Efficient Primal-Dual Reinforcement Learning for CMDPs with Non-stationary Objectives and Constraints
Yuhao Ding, Javad Lavaei
We consider primal-dual-based reinforcement learning (RL) in episodic constrained Markov decision processes (CMDPs) with non-stationary objectives and constraints, which plays a ce…
Beyond Exact Gradients: Convergence of Stochastic Soft-Max Policy Gradient Methods with Entropy Regularization
Yuhao Ding, Junzi Zhang, Hyunin Lee +1
Entropy regularization is an efficient technique for encouraging exploration and preventing a premature convergence of (vanilla) policy gradient methods in reinforcement learning (…
On the Global Optimum Convergence of Momentum-based Policy Gradient
Yuhao Ding, Junzi Zhang, Javad Lavaei
Policy gradient (PG) methods are popular and efficient for large-scale reinforcement learning due to their relative stability and incremental nature. In recent years, the empirical…
A Dual Approach to Constrained Markov Decision Processes with Entropy Regularization
Donghao Ying, Yuhao Ding, Javad Lavaei
We study entropy-regularized constrained Markov decision processes (CMDPs) under the soft-max parameterization, in which an agent aims to maximize the entropy-regularized value fun…
When Does MAML Objective Have Benign Landscape?
Igor Molybog, Javad Lavaei
The paper studies the complexity of the optimization problem behind the Model-Agnostic Meta-Learning (MAML) algorithm. The goal of the study is to determine the global convergence…
Sharp Restricted Isometry Bounds for the Inexistence of Spurious Local Minima in Nonconvex Matrix Recovery
Richard Y. Zhang, Somayeh Sojoudi, Javad Lavaei
Nonconvex matrix recovery is known to contain no spurious local minima under a restricted isometry property (RIP) with a sufficiently small RIP constant . If is too large, h…
How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery?
Richard Y. Zhang, Cédric Josz, Somayeh Sojoudi +1
When the linear measurements of an instance of low-rank matrix recovery satisfy a restricted isometry property (RIP)---i.e. they are approximately norm-preserving---the problem is…