Abstract: Methods of reinforcement learning for a resource management agent. Responsive to generated actions, corresponding observations are received. Each observation comprises a transition in a state associated with an inventory and an associated reward in the form of revenues generated from perishable resource sales. A randomized batch of observations is periodically sampled according to a prioritized replay sampling algorithm. A probability distribution for selection of observations within the batch is progressively adapted. Each batch of observations is used to update weight parameters of a neural network that comprises an approximator of the resource management agent, such that when provided with an input inventory state and an input action, an output of the neural network more closely approximates a true value of generating the input action while in the input inventory state. The neural network may be used to select each generated action depending upon a corresponding state associated with the inventory.

Abstract: A revenue management system comprising a first revenue management route which includes a forecast module and an optimization module and which calculates expected revenue for sales of inventor items based on historical data; a second revenue management route which is selected if the confidence in the forecasting in the first route is below a predetermined value.

Abstract: A revenue management system comprising a first revenue management route which includes a forecast module and an optimization module and which calculates expected revenue for sales of inventory items based on historical data; a second revenue management route which is selected if the confidence in the forecasting in the first route is below a predetermined value.