Bayesian Deep Reinforcement Learning via Deep Kernel Learning
- https://doi.org/10.2991/ijcis.2018.25905189How to use a DOI?
- Reinforcement learning; Uncertainty; Bayesian deep model; Gaussian process
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.
- © 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 -