Siu Tung Wong, Carlo Campajola · 2026 · arXiv
DOI: 10.48550/arXiv.2610.03598 · open-access PDF
Verstärktes Lernen (RL) wird zunehmend für finanzielle Optimierungskontrolle-Probleme verwendet, wenn komplexe Dynamiken analytische Strategien schwer zu erhalten machen. Es gibt finanzielle mathematische Literatur, die viele gelöste Modelle bietet, deren Gleichungen und Kontrollen das Lernen bewerten und leiten könnten; wir fragen, ob RL diese Ergebnisse ausnutzen kann. Wir platzieren einen Proximal Policy Optimization (PPO) Agent in einem analytisch gelösten Kontinuum Broker-Trader-Spiel.
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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