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This paper introduces RideSkill, a hierarchical algorithm designed for efficient ride-sharing that utilizes LLM-assisted automatic algorithmic design to overcome challenges in multi-agent reinforcement learning (MARL). By implementing a skill repository, a combiner for skill assignment, and a repositioner for vehicle relocation, RideSkill adapts to varying operational scenarios without the need for real-time LLM calls, thus enhancing performance in dynamic environments. The key result demonstrates that RideSkill significantly improves generalization and transferability in ride-sharing applications, addressing the limitations of previous MARL approaches.
RideSkill revolutionizes ride-sharing by enabling real-time adaptive dispatch without the overhead of constant LLM calls, enhancing both efficiency and scalability.
Ride-sharing, which allows multiple passengers with different origin-destination (OD) pairs to share a single vehicle, is a challenging operational problem, as it requires orders with different OD pairs to be efficiently bundled and assigned to vehicles under uncertain and varying scenarios. Although multi-agent reinforcement learning (MARL) solutions have achieved promising performance, they suffer from limited generalization (adapting to different environmental scenarios), low transferability (adapting to different platform objectives), and training difficulties in large-scale systems, such as the curse of dimensionality. Recently, motivated by the scaling of large language models (LLMs), several works have incorporated LLMs into ride-hailing systems, either by employing LLMs directly as decision-making agents or using them for automatic algorithm design. However, none of these approaches support vehicle sharing, which complicates the problem by expanding both the state and action spaces exponentially. Moreover, most of them require frequent LLM calls at inference time, making them infeasible for real-time deployment. To address these issues, we propose RideSkill, a hierarchical method for ride-sharing that leverages LLM-assisted automatic algorithmic design. RideSkill consists of a combiner that assigns appropriate skills to each vehicle from a learned skill repository, enabling adaptive dispatch under varying scenarios and objectives, and a repositioner that sequentially relocates idle vehicles to emerging regions, avoiding conflicts among vehicles. Crucially, the skill repository, combiner, and repositioner are all trained by an LLM-based automatic evolutionary method, eliminating the need for LLM calls during deployment and thus ensuring high real-time performance.
