Bayesian Deep Reinforcement Learning via Deep Kernel Learning

DOI

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/).

Download article (PDF)
View full text (HTML)

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  - 10.2991/ijcis.2018.25905189
ID  - Xuan2018
ER  -