Moira: Language-driven Hierarchical Reinforcement Learning for Pair Trading
Many sequential decision-making problems exhibit hierarchical structure, where high-level semantic choices constrain downstream actions and feedback is delayed and ambiguous. Learning in such settings is challenging due to credit assignment: performance degradation may arise from flawed abstractions, suboptimal execution, or their interaction. We study this challenge through pair trading, a domain that naturally combines long-horizon semantic reasoning for asset pair selection with short-horizon execution under partial observability. We formulate pair trading as a hierarchical reinforcement learning problem and propose a language-driven optimization framework in which both high-level and ...
| Authors | Polydoros Giannouris, Yuechen Jiang, Lingfei Qian et al. |
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| Primary category | cs.AI |
| Published | 3 May 2026 |
| Last updated at source | 3 May 2026 |
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