Abstract: A network system can receive location data from a provider device of a service provider. Using at least the location data in an optimization model, the network system can determine one or more actions for the service provider to optimize one or more metrics. The one or more metrics correspond to at least one of (i) an expected wait time for the service provider over a future period of time, (ii) an expected travel distance between providing services over a future period of time, or (iii) an expected amount of earnings for the service provider over a future period of time. The network system may then transmit a dataset to the provider device to display information corresponding to the one or more actions for the service provider.

Abstract: A network system can receive location data from a provider device of a service provider. Using at least the location data in an optimization model, the network system can determine one or more actions for the service provider to optimize one or more metrics. The one or more metrics correspond to at least one of (i) an expected wait time for the service provider over a future period of time, (ii) an expected travel distance between providing services over a future period of time, or (iii) an expected amount of earnings for the service provider over a future period of time. The network system may then transmit a dataset to the provider device to display information corresponding to the one or more actions for the service provider.

Abstract: A network system can receive location data from a provider device of a service provider. Using at least the location data in an optimization model, the network system can determine one or more actions for the service provider to optimize one or more metrics. The one or more metrics correspond to at least one of (i) an expected wait time for the service provider over a future period of time, (ii) an expected travel distance between providing services over a future period of time, or (iii) an expected amount of earnings for the service provider over a future period of time. The network system may then transmit a dataset to the provider device to display information corresponding to the one or more actions for the service provider.

Abstract: A network system can receive location data from a provider device of a service provider. Using at least the location data in an optimization model, the network system can determine one or more actions for the service provider to optimize one or more metrics. The one or more metrics correspond to at least one of (i) an expected wait time for the service provider over a future period of time, (ii) an expected travel distance between providing services over a future period of time, or (iii) an expected amount of earnings for the service provider over a future period of time. The network system may then transmit a dataset to the provider device to display information corresponding to the one or more actions for the service provider.

Abstract: Service providers can be identified to fulfill service requests of a network-based service. A network system is configured to generate, based on historical data associated with the network-based service, a machine-learned service provider optimization (MLSPO) model for generating service provider optimizations. The optimizations can include action recommendations that optimize one or more service metrics. The MLSPO model can be a reinforcement learning model generated by performing a plurality of simulations utilizing one or more virtual agents. A provider device of a service provider can transmit a set of data to the network system that indicates a current location of the service provider. Based on the current location and the MLSPO model, the network system can generate service provider optimizations. Optimization data can be transmitted to the provider device so that the provider device can display information corresponding to the service provider optimizations.

Abstract: Service providers can be identified to fulfill service requests of a network-based service. A network system is configured to generate, based on historical data associated with the network-based service, a machine-learned service provider optimization (MLSPO) model for generating service provider optimizations. The optimizations can include action recommendations that optimize one or more service metrics. The MLSPO model can be a reinforcement learning model generated by performing a plurality of simulations utilizing one or more virtual agents. A provider device of a service provider can transmit a set of data to the network system that indicates a current location of the service provider. Based on the current location and the MLSPO model, the network system can generate service provider optimizations. Optimization data can be transmitted to the provider device so that the provider device can display information corresponding to the service provider optimizations.

Abstract: Service providers can be identified to fulfill service requests of a network-based service. A network system is configured to generate, based on historical data associated with the network-based service, a machine-learned service provider optimization (MLSPO) model for generating service provider optimizations. The optimizations can include action recommendations that optimize one or more service metrics. The MLSPO model can be a reinforcement learning model generated by performing a plurality of simulations utilizing one or more virtual agents. A provider device of a service provider can transmit a set of data to the network system that indicates a current location of the service provider. Based on the current location and the MLSPO model, the network system can generate service provider optimizations. Optimization data can be transmitted to the provider device so that the provider device can display information corresponding to the service provider optimizations.

Abstract: Service providers can be identified to fulfill service requests of a network-based service. A network system is configured to generate, based on historical data associated with the network-based service, a machine-learned service provider optimization (MLSPO) model for generating service provider optimizations. The optimizations can include action recommendations that optimize one or more service metrics. The MLSPO model can be a reinforcement learning model generated by performing a plurality of simulations utilizing one or more virtual agents. A provider device of a service provider can transmit a set of data to the network system that indicates a current location of the service provider. Based on the current location and the MLSPO model, the network system can generate service provider optimizations. Optimization data can be transmitted to the provider device so that the provider device can display information corresponding to the service provider optimizations.