Sample-Based Planning for Continuous Action Markov Decision Processes

doi:10.1609/icaps.v21i1.13484

Sample-Based Planning for Continuous Action Markov Decision Processes

2011-03-22

0

OA

AI

DOI：10.1609/icaps.v21i1.13484

原文链接

原文求助

摘要

En 中文

In this paper, we present a new algorithm that integrates recent advances in solving continuous bandit problems with sample-based rollout methods for planning in Markov Decision Processes (MDPs). Our algorithm, Hierarchical Optimistic Optimization applied to Trees (HOOT) addresses planning in continuous-action MDPs. Empirical results are given that show that the performance of our algorithm meets or exceeds that of a similar discrete action planner by eliminating the problem of manual discretization of the action space.

Sample-Based Planning for Continuous Action Markov Decision Processes

摘要

期刊

机构

引用论文