International Journal of Computational Intelligence Systems

Volume 12, Issue 1, November 2018, Pages 164 - 171

Bayesian Deep Reinforcement Learning via Deep Kernel Learning

Authors
Junyu XuanJunyu.Xuan@uts.edu.au, Jie LuJie.Lu@uts.edu.au, Zheng YanYan.Zheng@uts.edu.au, Guangquan ZhangGuangquan.Zhang@uts.edu.au
Centre for Artificial Intelligence, University of Technology Sydney, Sydney, NSW 2007, Australia
Received 15 October 2018, Accepted 26 October 2018, Available Online 1 November 2018.
DOI
https://doi.org/10.2991/ijcis.2018.25905189How to use a DOI?
Keywords
Reinforcement learning; Uncertainty; Bayesian deep model; Gaussian process
Abstract

Reinforcement learning (RL) aims to resolve the sequential decision-making under uncertainty problem where an agent needs to interact with an unknown environment with the expectation of optimising the cumulative long-term reward. Many real-world problems could benefit from RL, e.g., industrial robotics, medical treatment, and trade execution. As a representative model-free RL algorithm, deep Q-network (DQN) has recently achieved great success on RL problems and even exceed the human performance through introducing deep neural networks. However, such classical deep neural network-based models cannot well handle the uncertainty in sequential decision-making and then limit their learning performance. In this paper, we propose a new model-free RL algorithm based on a Bayesian deep model. To be specific, deep kernel learning (i.e., a Gaussian process with deep kernel) is adopted to learn the hidden complex action-value function instead of classical deep learning models, which could encode more uncertainty and fully take advantage of the replay memory. The comparative experiments on standard RL testing platform, i.e., OpenAI-Gym, show that the proposed algorithm outweighs the DQN. Further investigations will be directed to applying RL for supporting dynamic decision-making in complex environments.

Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
12 - 1
Pages
164 - 171
Publication Date
2018/11/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
https://doi.org/10.2991/ijcis.2018.25905189How to use a DOI?
Copyright
© 2018, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Junyu Xuan
AU  - Jie Lu
AU  - Zheng Yan
AU  - Guangquan Zhang
PY  - 2018
DA  - 2018/11/01
TI  - Bayesian Deep Reinforcement Learning via Deep Kernel Learning
JO  - International Journal of Computational Intelligence Systems
SP  - 164
EP  - 171
VL  - 12
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2018.25905189
DO  - https://doi.org/10.2991/ijcis.2018.25905189
ID  - Xuan2018
ER  -