Action-specialized expert ensemble trading system with extended discrete action space using deep reinforcement learning.

ABSTRACT: Despite active research on trading systems based on reinforcement learning, the development and performance of research methods require improvements. This study proposes a new action-specialized expert ensemble method consisting of action-specialized expert models designed specifically for each reinforcement learning action: buy, hold, and sell. Models are constructed by examining and defining different reward values that correlate with each action under specific conditions, and investment behavior is reflected with each expert model. To verify the performance of this technique, profits of the proposed system are compared to those of single trading and common ensemble systems. To verify robustness and account for the extension of discrete action space, we compared and analyzed changes in p

SUBMITTER: Leem J 

PROVIDER: S-EPMC7384672 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature