Chapter 21: Reinforcement Learning
Policy Gradient Theorem and Actor-Critic Architectures advanced
How direct policy optimization turns delayed, noisy rewards into learning signals for continuous trading actions.
How direct policy optimization turns delayed, noisy rewards into learning signals for continuous trading actions.
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References
Understanding The Virtue of Complexity
Bryan T. Kelly, Semyon Malamud
(2025)
Actor-Critic Algorithms
Vijay Konda, John Tsitsiklis
(1999)
— MIT Press
Recent Advances in Reinforcement Learning in Finance
Ben Hambly, Renyuan Xu, Huining Yang
(2023)
Misspecification-Robust Inference in Linear Asset-Pricing Models with Irrelevant Risk Factors
Nikolay Gospodinov, Raymond Kan, Cesare Robotti
(2014)
— The Review of Financial Studies
Proximal Policy Optimization Algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, Oleg Klimov
(2017)
Policy Gradient Methods for Reinforcement Learning with Function Approximation
Richard S Sutton, David A. McAllester, Satinder P. Singh, Yishay Mansour, S. A. Solla, T. K. Leen, K. Müller
(2000)
— MIT Press