50 citations · 194 across the 41 of their papers we have counts for
46 papers
Internal State-Based Policy Gradient Methods for Partially Observable Markov Potential Games
Wonseok Yang, Thinh T. Doan
This letter studies multi-agent reinforcement learning in partially observable Markov potential games. Solving this problem is challenging due to partial observability, decentraliz…
Finite-Time Analysis of Projected Two-Time-Scale Stochastic Approximation
Yitao Bai, Thinh T. Doan, Justin Romberg
We study the finite-time convergence of projected linear two-time-scale stochastic approximation with constant step sizes and Polyak--Ruppert averaging. We establish an explicit me…
Accelerating Multi-Task Temporal Difference Learning under Low-Rank Representation
Yitao Bai, Sihan Zeng, Justin Romberg +1
We study policy evaluation problems in multi-task reinforcement learning (RL) under a low-rank representation setting. In this setting, we are given learning tasks where the co…
Nonasymptotic CLT and Error Bounds for Linear Two-Time-Scale Stochastic Approximation
Seo Taek Kong, Sihan Zeng, Thinh T. Doan +1
We consider linear two-time-scale stochastic approximation algorithms driven by martingale noise. Recent applications in machine learning motivate the need to understand finite-tim…
Accelerated Multi-Time-Scale Stochastic Approximation: Optimal Complexity and Applications in Reinforcement Learning and Multi-Agent Games
Sihan Zeng, Thinh T. Doan
Multi-time-scale stochastic approximation is an iterative algorithm for finding the fixed point of a set of coupled operators given their noisy samples. It has been observed th…
Resilient Two-Time-Scale Local Stochastic Gradient Descent for Byzantine Federated Learning
Amit Dutta, Thinh T. Doan
We study local stochastic gradient descent methods for solving federated optimization over a network of agents communicating indirectly through a centralized coordinator. We are in…