11 citations · 26 across the 12 of their papers we have counts for
4 papers · 1 filter
Learn Quasi-stationary Distributions of Finite State Markov Chain
Zhiqiang Cai, Ling Lin, Xiang Zhou
We propose a reinforcement learning (RL) approach to compute the expression of quasi-stationary distribution. Based on the fixed-point formulation of quasi-stationary distribution,…
Sublinear Regret for Learning POMDPs
Yi Xiong, Ningyuan Chen, Xuefeng Gao +1
We study the model-based undiscounted reinforcement learning for partially observable Markov decision processes (POMDPs). The oracle we consider is the optimal policy of the POMDP…
Roughness Index for Loss Landscapes of Neural Network Models of Partial Differential Equations
Keke Wu, Xiangru Jian, Rui Du +2
Loss landscape is a useful tool to characterize and compare neural network models. The main challenge for analysis of loss landscape for the deep neural networks is that they are g…
Value-Gradient based Formulation of Optimal Control Problem and Machine Learning Algorithm
Alain Bensoussan, Jiayue Han, Sheung Chi Phillip Yam +1
Optimal control problem is typically solved by first finding the value function through Hamilton-Jacobi equation (HJE) and then taking the minimizer of the Hamiltonian to obtain th…