2 citations · 2 across the 4 of their papers we have counts for
5 papers
A Two Stage Adaptive Metropolis Algorithm
Anirban Mondal, Kai Yin, Abhijit Mandal
We propose a new sampling algorithm combining two quite powerful ideas in the Markov chain Monte Carlo literature -- adaptive Metropolis sampler and two-stage Metropolis-Hastings s…
Online Planning in Uncertain and Dynamic Environment in the Presence of Multiple Mobile Vehicles
Junhong Xu, Kai Yin, Lantao Liu
We investigate the autonomous navigation of a mobile robot in the presence of other moving vehicles under time-varying uncertain environmental disturbances. We first predict the fu…
Kernel Taylor-Based Value Function Approximation for Continuous-State Markov Decision Processes
Junhong Xu, Kai Yin, Lantao Liu
We propose a principled kernel-based policy iteration algorithm to solve the continuous-state Markov Decision Processes (MDPs). In contrast to most decision-theoretic planning fram…
Reachable Space Characterization of Markov Decision Processes with Time Variability
Junhong Xu, Kai Yin, Lantao Liu
We propose a solution to a time-varying variant of Markov Decision Processes which can be used to address decision-theoretic planning problems for autonomous systems operating in u…
State-Continuity Approximation of Markov Decision Processes via Finite Element Methods for Autonomous System Planning
Junhong Xu, Kai Yin, Lantao Liu
Motion planning under uncertainty for an autonomous system can be formulated as a Markov Decision Process with a continuous state space. In this paper, we propose a novel solution…