Virtual Environment for Ride-Hailing Policy Optimization
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Solution Overview
Problem
Reinforcement learning for driver incentive program recommendation in ride-hailing platforms is impractical due to high trial-and-error costs and the influence of unobservable factors in the ride-hailing environment, such as hidden confounders.
Innovation Solution
A virtual environment is constructed using historical interaction trajectories, with a simulator integrating a platform policy, confounding policy, and driver policy to simulate interactions and approximate the real-world data distribution, incorporating a reward function trained with an uplift inference network to optimize program recommendation policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning is applied to train driver incentive program recommendation policies in a real ride-hailing environment, then the policy optimization capability is improved, but the trial-and-error cost becomes prohibitively high
Solution Approach 1:
The patent creates a virtual environment that copies the essential dynamics and characteristics of the real ride-hailing environment. This virtual copy allows reinforcement learning to be performed on simulated data rather than real-world interactions, thereby maintaining policy optimization capability while eliminating the high trial-and-error costs associated with real-world testing.
2Loss of time
If a virtual environment is constructed to simulate real-world interactions, then the trial-and-error cost is reduced, but the complexity of the system increases due to the need to model confounding factors
Solution Approach 1:
The patent extracts and isolates the confounding factors from the complex real-world environment and models them separately within the virtual environment. By taking out these difficult-to-model elements and representing them explicitly through confounding policies and variables, the system can simulate their effects without needing to replicate the entire complexity of the real world, thus reducing overall system complexity while maintaining simulation accuracy.
3Device complexity
If the simulator models only observable factors, then the system complexity is reduced, but the measurement precision of driver behavior prediction deteriorates due to unobservable confounders
Solution Approach 1:
The patent introduces confounding policies and confounding variables as intermediary elements that mediate between observable factors and driver behaviors. These intermediaries represent the unobservable confounding factors and allow the simulator to capture their influence on driver decisions without directly observing them, thereby improving behavior prediction accuracy while maintaining manageable system complexity.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for constructing a virtual environment for a ride-hailing platform are disclosed. An exemplary method comprises: obtaining a plurality of historical interaction trajectories each comprising one or more interaction records between a driver and a ride-hailing platform, each interaction record comprising a program recommendation of the ride-hailing platform to the driver and a reaction of the driver in response to the program recommendation; training a simulator based on the plurality of historical interaction trajectories; and integrating a reward function with the simulator to construct the virtual environment, wherein the plurality of first program recommendations and the plurality of reactions form a plurality of simulated interactions, and a data distribution of the plurality of simulated interactions approximates a data distribution of a plurality of interaction records in the plurality of historical interaction trajectories.