عنوان انگلیسی مقاله:
Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets
ترجمه فارسی عنوان مقاله:
رویای یادگیری ماشین: پسوندهای Lipschitz برای یادگیری تقویتی در بازارهای مالی
Sciencedirect - Elsevier - Neurocomputing, 398 (2020) 172-184. doi:10.1016/j.neucom.2020.02.052
J.M. Calabuig, H. Falciani, E.A. Sánchez-Pérez
We consider a quasi-metric topological structure for the construction of a new reinforcement learning model in the framework of financial markets. It is based on a Lipschitz type extension of reward func- tions defined in metric spaces. Specifically, the McShane and Whitney extensions are considered for a reward function which is defined by the total evaluation of the benefits produced by the investment decision at a given time. We define the metric as a linear combination of a Euclidean distance and an angular metric component. All information about the evolution of the system from the beginning of the time interval is used to support the extension of the reward function, but in addition this data set is enriched by adding some artificially produced states. Thus, the main novelty of our method is the way we produce more states—which we call “dreams”—to enrich learning. Using some known states of the dynamical system that represents the evolution of the financial market, we use our technique to sim- ulate new states by interpolating real states and introducing some random variables. These new states are used to feed a learning algorithm designed to improve the investment strategy by following a typical reinforcement learning scheme.
Keywords: Pseudo-metric | Reinforcement learning | Lipschitz extension | Mathematical economics | Financial market | Model