3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.AI2019★ 3 cited
Online Learning and Planning in Partially Observable Domains without Prior Knowledge
Yunlong Liu, Jianyang Zheng
How an agent can act optimally in stochastic, partially observable domains is a challenge problem, the standard approach to address this issue is to learn the domain model firstly…
cs.AI2019★ 1 cited
Combining Offline Models and Online Monte-Carlo Tree Search for Planning from Scratch
Yunlong Liu, Jianyang Zheng
Planning in stochastic and partially observable environments is a central issue in artificial intelligence. One commonly used technique for solving such a problem is by constructin…
cs.LG2016
Selecting Bases in Spectral learning of Predictive State Representations via Model Entropy
Yunlong Liu, Hexing Zhu
Predictive State Representations (PSRs) are powerful techniques for modelling dynamical systems, which represent a state as a vector of predictions about future observable events (…