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Hi-DARTS: Hierarchical Dynamically Adapting Reinforcement Trading System
Published in International Conference on ICT Convergence (ICTC 2025), 2025
Conventional autonomous trading systems struggle to balance computational efficiency and market responsiveness due to their fixed operating frequency. We propose Hi-DARTS, a hierarchical multi-agent reinforcement learning framework in which a meta-agent monitors market volatility and activates frequency-specialized sub-agents, outperforming buy-and-hold baselines in backtesting.
Recommended citation: Hoon Sagong, Heesu Kim, and Hanbeen Hong. (2025). "Hi-DARTS: Hierarchical Dynamically Adapting Reinforcement Trading System." International Conference on ICT Convergence (ICTC 2025).
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