Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets
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We develop a new topological structure for the construction of a reinforcement learning model in the framework of financial markets. It is based on Lipschitz type extension of reward functions defined in metric spaces. Using some known states of a dynamical system that represents the evolution of a financial market, we use our technique to simulate new states, that we call "dreams". These new states are used to feed a learning algorithm designed to improve the investment strategy.
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