Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications

Adaptively Finding Optimal Routes under Principles of Spatial Cognition-A Hierarchical Reinforcement Learning Approach

Authors
Weifeng Zhao, Qin Zhang
Corresponding Author
Weifeng Zhao
Available Online January 2016.
DOI
https://doi.org/10.2991/icaita-16.2016.56How to use a DOI?
Keywords
spatial cognition; route selection; hierarchical reinforcement learning; pre-learning; real-time learning
Abstract
Way finding research has paid much attention to the selection of optimal routes under principles of spatial cognition. However, the commonly employed implemental approaches suffer inevitably from the contradictions between personalized network modelling and network data sharing. This paper presents one kind of interactive route selection approach based on hierarchical reinforcement learning. In this approach, a complete network model is unnecessary, but the environmental states are automatically perceived by the agent and then mapped into the reward function defining the goal of cognitively optimal routes. The optimal routes corresponding to the policies with maximal cumulative rewards can be found through a two-stage learning process including a pre-learning stage and a real-time learning one. Our experimental results show that the proposed approach learns effectively enough for real-time route selection and ensures found routes close to global optimal ones.
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This is an open access article distributed under the CC BY-NC license.

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Volume Title
Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
Series
Advances in Intelligent Systems Research
Publication Date
January 2016
ISBN
978-94-6252-162-9
ISSN
1951-6851
DOI
https://doi.org/10.2991/icaita-16.2016.56How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Weifeng Zhao
AU  - Qin Zhang
PY  - 2016/01
DA  - 2016/01
TI  - Adaptively Finding Optimal Routes under Principles of Spatial Cognition-A Hierarchical Reinforcement Learning Approach
BT  - Proceedings of the 2016 International Conference on Artificial Intelligence: Technologies and Applications
PB  - Atlantis Press
SP  - 227
EP  - 230
SN  - 1951-6851
UR  - https://doi.org/10.2991/icaita-16.2016.56
DO  - https://doi.org/10.2991/icaita-16.2016.56
ID  - Zhao2016/01
ER  -