Siu Tung Wong, Carlo Campajola · 2026 · arXiv
DOI: 10.48550/arXiv.2610.03598 · open-access PDF
A aprendizagem reforçada (RL) é cada vez mais usada para problemas de controle financeiro óptimo quando as dinâmicas complexas tornam as estratégias analíticas difíceis de obter. Há literatura matemática financeira que fornece muitos modelos resolvidos cujas equações e controles poderiam avaliar e orientar a aprendizagem; perguntamos se a RL pode explorar esses resultados. Colocamos um agente de otimização de políticas proximal (PPO) em um jogo de corretor contínuo-tempo analíticamente resolvido.
Machine translation (Meta M2M100); the English original follows.
Reinforcement learning (RL) is increasingly used for financial optimal-control problems when complex dynamics make analytical strategies difficult to obtain. There are financial mathematics literactures which provides many solved models whose equations and controls could evaluate and guide learning; we ask whether RL can exploit these results. We place a proximal policy optimisation (PPO) agent in an analytically solved continuous-time broker--trader game. PPO replaces the broker and chooses its trading speed while interacting with an informed trader and stochastic uninformed order flow. We de
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