Many-objective stochastic path finding using reinforcement learning
作者：
Highlights：
• A novel many-objective reinforcement learning algorithm is proposed.
• A benchmark many-objective pathfinding problem is introduced.
• We evaluated the algorithm on path finding problems with five and six objectives.
• Total reward obtained, solution set quality, and episode duration were measured.
• The proposed method outperforms the state of the art on all problems.
摘要
•A novel many-objective reinforcement learning algorithm is proposed.•A benchmark many-objective pathfinding problem is introduced.•We evaluated the algorithm on path finding problems with five and six objectives.•Total reward obtained, solution set quality, and episode duration were measured.•The proposed method outperforms the state of the art on all problems.
论文关键词：Many objective reinforcement learning,Stochastic path finding,Sequential decision making under uncertainty,Social choice theory
论文评审过程：Received 22 April 2016, Revised 18 October 2016, Accepted 18 October 2016, Available online 4 November 2016, Version of Record 2 January 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.10.045
