Deep Reinforcement Learning for Vehicle Repositioning
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Solution Overview
Problem
Existing vehicle repositioning systems on mobility-on-demand platforms face challenges in efficiently managing human-driven vehicles, as drivers may not follow instructions and have voluntary idle-time repositioning, leading to reduced operational efficiency and income for drivers.
Innovation Solution
A deep reinforcement learning-based method that uses a spatiotemporal deep value-network to learn a state-value function for vehicle repositioning, combining offline batch reinforcement learning with decision-time planning to optimize vehicle repositioning actions, recommending next steps based on expected cumulative rewards and heuristic tree search.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If autonomous vehicle management systems are used with full compliance assumption, then system control and routing efficiency are improved, but the system fails to account for human driver voluntary behavior and instruction non-compliance
Solution Approach 1:
The patent transforms the repositioning problem from a control-based approach to an incentive-based approach by changing the parameter of driver motivation. Instead of assuming full compliance with repositioning instructions, the system uses reinforcement learning to calculate incentive amounts that voluntarily motivate drivers to follow repositioning routes, thereby adapting to human driver behavior while maintaining routing efficiency
Solution Approach 2:
The patent inverts the traditional control paradigm by not directly controlling driver behavior but instead creating an incentive structure that leads drivers to self-motivate and self-regulate their repositioning actions. The reinforcement learning agent learns to provide incentives rather than issuing commands, fundamentally changing the control mechanism from top-down instruction to bottom-up voluntary compliance
2Measurement precision
If complex reinforcement learning models are deployed for real-time repositioning decisions, then repositioning accuracy and driver income optimization are improved, but computational complexity and implementation difficulty increase
Solution Approach 1:
The patent segments the complex reinforcement learning problem into two distinct phases: an offline training phase where the deep value network is trained using batch reinforcement learning on historical data, and an online inference phase where the pre-trained model provides real-time repositioning recommendations. This segmentation allows computationally intensive training to occur separately from time-critical deployment, reducing real-time computational complexity while maintaining high repositioning accuracy
Solution Approach 2:
The patent performs preliminary action by pre-training the deep value network offline using batch reinforcement learning before deployment. The model learns optimal repositioning policies from historical data in advance, so that during real-time operation, only lightweight inference is required. This preliminary training phase captures complex patterns that would be difficult to compute in real-time, thereby reducing online computational complexity while preserving accuracy
Data Source
AI summary
Deep reinforcement learning may be used for vehicle repositioning on mobility-on-demand platforms. Information may be obtained. The information may include a current location of a vehicle on a ride-sharing platform. A set of paths originated from the current location of the vehicle may be obtained. Each of the set of paths may have a length less than a preset maximum path length. A set of expected cumulative rewards along the set of paths may be obtained based on a trained deep value-network. A best path from the set of paths may be selected based on a heuristic tree search of the set of expected cumulative rewards. A next step along the best path may be recommended as a reposition action for the vehicle.