DEEP REINFORCEMENT LEARNING FOR OPTIMIZING CARPOOLING POLICIES

1 . A method for operating a ride-share-enabled vehicle comprising:

determining a target location of the ride-share-enabled vehicle;

determining a ride-sharing policy algorithm to determine a behavior of the ride-share-enabled vehicle including whether to accept a multiple shared ride or maintain a single shared ride and a route of the multiple shared ride, based on the determined target location of the ride-share-enabled vehicle;

determining a behavior of the ride-share-enabled vehicle based on a current location of the ride-share-enabled vehicle and the determined ride-sharing policy algorithm; and

causing the ride-share-enabled vehicle to be operated according to the determined behavior of the ride-share-enabled vehicle.

2 . The method of claim 1 , wherein the determined ride-sharing policy algorithm is configured based on a deep reinforced learning method of a deep Q-Networks (DQN).

3 . The method of claim 1 , further comprising determining a current date or a current time, wherein the ride-sharing policy algorithm is determined also based on the current date or the current time.

4 . The method of claim 1 , wherein the determining the ride-sharing policy algorithm comprises:

determining a first ride-sharing policy algorithm as the ride-sharing policy algorithm, when the target location is a first location; and

determining a second ride-sharing policy algorithm different from the first ride-sharing policy algorithm as the ride-sharing policy algorithm, when the target location is a second location different from the first location.

5 . The method of claim 4 , wherein the first location is more populated than the second location, and the first ride-sharing policy algorithm is configured to accept more multiple shared rides than the second ride-sharing policy algorithm.

6 . The method of claim 5 , wherein the first ride-sharing policy algorithm is not configured based on a deep reinforced learning method of a deep Q-Networks (DQN), and the second ride-sharing policy algorithm is configured based on the deep reinforced learning method of the DQN.

7 . The method of claim 1 , further comprising determining a ride request density at the determined target location of the ride-share-enabled vehicle, wherein the ride-sharing policy algorithm is determined based on the determined ride request density.

8 . The method of claim 7 , further comprising determining a current date or a current time, wherein the ride request density at the determined target location of the ride-share-enabled vehicle is determined based on the current date or the current time.

9 . The method of claim 7 , wherein the determining the ride-sharing policy algorithm comprises:

determining a first ride-sharing policy algorithm as the ride-sharing policy algorithm, when the ride request density is a first density; and

determining a second ride-sharing policy algorithm different from the first ride-sharing policy algorithm as the ride-sharing policy algorithm, when the ride request density is a second density less dense than the first location.

10 . The method of claim 9 , wherein the first ride-sharing policy algorithm is configured to accept more multiple shared rides than the second ride-sharing policy algorithm.

11 . The method of claim 10 , wherein the first ride-sharing policy algorithm is not configured based on a deep reinforced learning method of a deep Q-Networks (DQN), and the second ride-sharing policy algorithm is configured based on the deep reinforced learning method of the DQN.

12 . The method of claim 1 , wherein the target location of the ride-share-enabled vehicle comprises a target service region for a ride share service.

13 . The method of claim 1 , wherein the target location of the ride-share-enabled vehicle comprises the current location of the ride-share-enabled vehicle.

14 . A non-transitory computer-readable storage medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform a method for operating a ride-share-enabled vehicle, the method comprising:

determining a target location of the ride-share-enabled vehicle;

determining a ride-sharing policy algorithm to determine a behavior of the ride-share-enabled vehicle including whether to accept a multiple shared ride or maintain a single shared ride and a route of the multiple shared ride, based on the determined target location of the ride-share-enabled vehicle;

determining a behavior of the ride-share-enabled vehicle based on a current location of the ride-share-enabled vehicle and the determined ride-sharing policy algorithm; and

causing the ride-share-enabled vehicle to be operated according to the determined behavior of the ride-share-enabled vehicle.

15 . The non-transitory computer-readable storage medium of claim 14 , wherein the determined ride-sharing policy algorithm is configured based on a deep reinforced learning method of a deep Q-Networks (DQN).

16 . The non-transitory computer-readable storage medium of claim 14 , wherein the method further comprises determining a current date or a current time, wherein the ride-sharing policy algorithm is determined also based on the current date or the current time.

17 . The non-transitory computer-readable storage medium of claim 14 , wherein the method further comprises determining a ride request density at the determined target location of the ride-share-enabled vehicle, wherein the ride-sharing policy algorithm is determined based on the determined ride request density.

18 . A system for providing a ride-share service comprising:

a server including one or more processors and memory storing instructions that, when executed by one or more processors, cause the one or more processors to perform a method for operating one or more ride-share-enabled vehicles, wherein the method comprises:

determining a target location of a target vehicle of the one or more ride-share-enabled vehicles;

determining a ride-sharing policy algorithm to determine a behavior of the target vehicle including whether to accept a multiple shared ride or maintain a single shared ride and a route of the multiple shared ride, if any, based on the determined target location of the target vehicle;

determining a behavior of the target vehicle based on a current location of the target vehicle and the determined ride-sharing policy algorithm; and

causing the target vehicle to be operated according to the determined behavior of the target vehicle.

19 . The system of claim 18 , wherein at least one of the one or more ride-share-enabled vehicles is an autonomous vehicle.

20 . The system of claim 18 , wherein the determined ride-sharing policy algorithm is configured based on a deep reinforced learning method of a deep Q-Networks (DQN).