5 papers
Non-Asymptotic Performance Guarantees for MLE under a Gumbel-Softmax Index Model with Phaseless Measurements
Jinchi Chen, Mingxi Hu, Peigang Jiang +3
This paper studies the recovery of a complex-valued signal from index-only observations generated by a phaseless Gumbel-Softmax model. The model is motivated by limited-feedback fr…
Global Convergence of Natural Policy Gradient with Hessian-aided Momentum Variance Reduction
Jie Feng, Ke Wei, Jinchi Chen
Natural policy gradient (NPG) and its variants are widely-used policy search methods in reinforcement learning. Inspired by prior work, a new NPG variant coined NPG-HM is developed…
A Zeroth-Order Variance-Reduced Method for Decentralized Stochastic Non-convex Optimization
Hongxu Chen, Jinchi Chen, Ke Wei
In this paper, we consider a distributed stochastic non-convex optimization problem, which is about minimizing a sum of local cost functions over a network with only zeroth-ord…
On the Linear Convergence of Policy Gradient under Hadamard Parameterization
Jiacai Liu, Jinchi Chen, Ke Wei
The convergence of deterministic policy gradient under the Hadamard parameterization is studied in the tabular setting and the linear convergence of the algorithm is established. T…
Decentralized Natural Policy Gradient with Variance Reduction for Collaborative Multi-Agent Reinforcement Learning
Jinchi Chen, Jie Feng, Weiguo Gao +1
This paper studies a policy optimization problem arising from collaborative multi-agent reinforcement learning in a decentralized setting where agents communicate with their neighb…