Learning Options from Demonstration using Skill Segmentation

Autor:	Cockcroft, Matthew, Mawjee, Shahil, James, Steven, Ranchod, Pravesh
Rok vydání:	2020
Předmět:	Computer Science - Machine Learning Statistics - Machine Learning
Druh dokumentu:	Working Paper
Popis:	We present a method for learning options from segmented demonstration trajectories. The trajectories are first segmented into skills using nonparametric Bayesian clustering and a reward function for each segment is then learned using inverse reinforcement learning. From this, a set of inferred trajectories for the demonstration are generated. Option initiation sets and termination conditions are learned from these trajectories using the one-class support vector machine clustering algorithm. We demonstrate our method in the four rooms domain, where an agent is able to autonomously discover usable options from human demonstration. Our results show that these inferred options can then be used to improve learning and planning. Comment: To be published in SAUPEC/RobMech/PRASA 2020. Consists of 6 pages, 5 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2001.06793 Zobrazit plný text záznamu View this record from Arxiv