10 citations · 10 across the 5 of their papers we have counts for
5 papers
Federated Control in Markov Decision Processes
Hao Jin, Yang Peng, Liangyu Zhang +1
We study problems of federated control in Markov Decision Processes. To solve an MDP with large state space, multiple learning agents are introduced to collaboratively learn its op…
Federated Reinforcement Learning with Constraint Heterogeneity
Hao Jin, Liangyu Zhang, Zhihua Zhang
We study a Federated Reinforcement Learning (FedRL) problem with constraint heterogeneity. In our setting, we aim to solve a reinforcement learning problem with multiple constraint…
Federated Reinforcement Learning with Environment Heterogeneity
Hao Jin, Yang Peng, Wenhao Yang +2
We study a Federated Reinforcement Learning (FedRL) problem in which agents collaboratively learn a single policy without sharing the trajectories they collected during agent-e…
Meta-Regularization: An Approach to Adaptive Choice of the Learning Rate in Gradient Descent
Guangzeng Xie, Hao Jin, Dachao Lin +1
We propose \textit{Meta-Regularization}, a novel approach for the adaptive choice of the learning rate in first-order gradient descent methods. Our approach modifies the objective…
Towards Better Generalization: BP-SVRG in Training Deep Neural Networks
Hao Jin, Dachao Lin, Zhihua Zhang
Stochastic variance-reduced gradient (SVRG) is a classical optimization method. Although it is theoretically proved to have better convergence performance than stochastic gradient…