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  .
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 functions 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 simulate 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 .
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develop a new
consider a quasi-metric
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new
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a
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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.
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a
the
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a
the
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, that we call ``dreams"
by interpolating real states and introducing some random variables
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by following a typical reinforcement learning scheme