Abstract: A method of determining an action of a device for a given situation, implemented by a computer system, includes for a learning model that learns a distribution of rewards according to the action of the device for the situation using a risk-measure parameter associated with control of the device, selectively setting a value of the risk-measure parameter in accordance with an environment in which the device is controlled; and determining the action of the device for the given situation when controlling the device in the environment, based on the set value of the risk-measure parameter.

Abstract: A method, non-transitory computer readable medium, and apparatus for dynamic pricing of a parking space are disclosed. For example, the method determines a fraction high fraction low (FHFL) based on one or more co-variates for a future time interval, determines a rate change for the parking space based on the FHFL, applies the rate change to a pre-calculated rate for the parking space for the future time interval to determine an updated rate and publishes the updated rate.