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cs.LG2025
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…
cs.LG2020★ 6 cited
Local Stochastic Approximation: A Unified View of Federated Learning and Distributed Multi-Task Reinforcement Learning Algorithms
Thinh T. Doan
Motivated by broad applications in reinforcement learning and federated learning, we study local stochastic approximation over a network of agents, where their goal is to find the…
cs.LG2020★ 2 cited
Finite-Time Analysis and Restarting Scheme for Linear Two-Time-Scale Stochastic Approximation
Thinh T. Doan
Motivated by their broad applications in reinforcement learning, we study the linear two-time-scale stochastic approximation, an iterative method using two different step sizes for…