Multi-objective distributional reinforcement learning for large-scale order dispatching

A system for order dispatching, comprising one or more processors and one or more non-transitory computer-readable memories coupled to the one or more processors and configured with instructions executable by the one or more processors to cause the system to perform operations comprising:

• obtaining a set of historical driver trajectories, wherein each trajectory in the set of historical driver trajectories comprises a sequence of states and actions of a historical driver;
• determining a weight vector between a first reward of the set of historical driver trajectories and a second reward of the set of historical driver trajectories using inverse reinforcement learning (IRL);
• jointly learning a first value function and a second value function using distributional reinforcement learning (DRL) based on the historical driver trajectories and the weight vector, wherein the first value function and the second value function comprise distributions of expected returns of future dispatch decisions;
• obtaining a set of driver-order pairs, wherein each driver-order pair of the set of driver-order pairs comprises a driver and a pending order;
• determining a set of scores comprising a score of each driver-order pair in the set of driver-order pairs based on the weight vector, the first value function, and the second value function;
• and
• determining a set of dispatch decisions based on the set of scores that maximizes a total reward of the set of dispatch decisions, wherein each dispatch decision in the set of dispatch decisions comprises at least matching an available driver to a passenger.

A non-transitory computer-readable storage medium configured with instructions executable by one or more processors to cause the one or more processors to perform operations comprising:

• obtaining a set of historical driver trajectories, wherein each trajectory in the set of historical driver trajectories comprises a sequence of states and actions of a historical driver;
• determining a weight vector between a first reward of the set of historical driver trajectories and a second reward of the set of historical driver trajectories using inverse reinforcement learning (IRL);
• jointly learning a first value function and a second value function using distributional reinforcement learning (DRL) based on the historical driver trajectories and the weight vector, wherein the first value function and the second value function comprise distributions of expected returns of future dispatch decisions;
• obtaining a set of driver-order pairs, wherein each driver-order pair of the set of driver-order pairs comprises a driver and a pending order;
• determining a set of scores comprising a score of each driver-order pair in the set of driver-order pairs based on the weight vector, the first value function, and the second value function;
• and
• determining a set of dispatch decisions based on the set of scores that maximizes a total reward of the set of dispatch decisions, wherein each dispatch decision in the set of dispatch decisions comprises at least matching an available driver to a passenger.