6 citations · 12 across the 6 of their papers we have counts for
6 papers
Model-free Learning with Heterogeneous Dynamical Systems: A Federated LQR Approach
Han Wang, Leonardo F. Toso, Aritra Mitra +1
We study a model-free federated linear quadratic regulator (LQR) problem where M agents with unknown, distinct yet similar dynamics collaboratively learn an optimal policy to minim…
Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback
Hang Wang, Sen Lin, Junshan Zhang
The ensemble method is a promising way to mitigate the overestimation issue in Q-learning, where multiple function approximators are used to estimate the action values. It is known…
Warm-Start Actor-Critic: From Approximation Error to Sub-optimality Gap
Hang Wang, Sen Lin, Junshan Zhang
Warm-Start reinforcement learning (RL), aided by a prior policy obtained from offline training, is emerging as a promising RL approach for practical applications. Recent empirical…
Learning Personalized Models with Clustered System Identification
Leonardo F. Toso, Han Wang, James Anderson
We address the problem of learning linear system models from observing multiple trajectories from different system dynamics. This framework encompasses a collaborative scenario whe…
Distributed Q-Learning with State Tracking for Multi-agent Networked Control
Hang Wang, Sen Lin, Hamid Jafarkhani +1
This paper studies distributed Q-learning for Linear Quadratic Regulator (LQR) in a multi-agent network. The existing results often assume that agents can observe the global system…
System Identification via Meta-Learning in Linear Time-Varying Environments
Sen Lin, Hang Wang, Junshan Zhang
System identification is a fundamental problem in reinforcement learning, control theory and signal processing, and the non-asymptotic analysis of the corresponding sample complexi…