A reinforcement learning approach to the shepherding task using SARSA

Clark Kendrick Go, Bryan Lao, Junichiro Yoshimoto, Kazushi Ikeda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

14 Citations (Scopus)

Abstract

In this paper, we present a reinforcement learning model of the shepherding of a flock of sheep by a dog. The shepherding task, a heuristic model originally proposed by Strombom, et al., describes the dynamics of the sheep while being herded by a dog to a predefined target. This study recreates the proposed model using SARSA, an algorithm for learning the optimal policy in reinforcement learning. Results show that with a discretized state and action space, the dog is able to successfully herd a flock of a sheep to the target position by first learning to reach a subgoal. A reward is awarded when the dog reaches the neighbourhood of a subgoal, while a penalty is incurred for each time the shepherding task is not completed. The stochasticity of the interaction among sheep and dog, including the existence of multiple subgoals affect the learning time of the agent. Finally, we present an example of the learned shepherding task which shows the agent's continuous success after the 350th episode.

Original languageEnglish
Title of host publication2016 International Joint Conference on Neural Networks, IJCNN 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3833-3836
Number of pages4
ISBN (Electronic)9781509006199
DOIs
Publication statusPublished - 31-10-2016
Externally publishedYes
Event2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada
Duration: 24-07-201629-07-2016

Publication series

NameProceedings of the International Joint Conference on Neural Networks
Volume2016-October

Conference

Conference2016 International Joint Conference on Neural Networks, IJCNN 2016
Country/TerritoryCanada
CityVancouver
Period24-07-1629-07-16

All Science Journal Classification (ASJC) codes

  • Software
  • Artificial Intelligence

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