A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning
Abstract Deep reinforcement learning (DRL) has been extensively used to address portfolio optimization problems. DRL agents acquire knowledge and make decisions through unsupervised interactions with their environment without requiring explicit knowledge of the joint dynamics of portfolio assets. Am...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-05-01
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| Series: | Complex & Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40747-025-01884-y |
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