Hierarchical Model-Based Deep Reinforcement Learning for Single-Asset Trading
We present a hierarchical reinforcement learning (RL) architecture that employs various low-level agents to act in the trading environment, i.e., the market. The highest-level agent selects from among a group of specialized agents, and then the selected agent decides when to sell or buy a single ass...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Analytics |
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| Online Access: | https://www.mdpi.com/2813-2203/2/3/31 |