The explosive price volatility from the end of 2017 to January 2018 shows that bitcoin is a high risk asset. The deep reinforcement algorithm is straightforward idea for directly outputs the market management actions to achieve higher profit instead of higher price-prediction accuracy. However, existing deep reinforcement learning algorithms including Q-learning are also limited to problems caused by enormous searching space. We propose a combination of double Q-network and unsupervised pre-training using Deep Boltzmann Machine (DBM) to generate and enhance the optimal Q-function in cryptocurrency trading. We obtained the profit of 2,686% in simulation, whereas the best conventional model had that of 2,087% for the same period of test. In addition, our model records 24% of profit while market price significantly drops by −64%.
|Title of host publication||Intelligent Data Engineering and Automated Learning – IDEAL 2018 - 19th International Conference, Proceedings|
|Editors||Hujun Yin, Paulo Novais, David Camacho, Antonio J. Tallón-Ballesteros|
|Number of pages||13|
|Publication status||Published - 2018|
|Event||19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018 - Madrid, Spain|
Duration: 2018 Nov 21 → 2018 Nov 23
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Other||19th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2018|
|Period||18/11/21 → 18/11/23|
Bibliographical noteFunding Information:
This research was supported by Korea Electric Power Corporation. (Grant number: R18XA05).
This research was supported by Korea Electric Power Corporation. (Grant
© 2018, Springer Nature Switzerland AG.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Computer Science(all)