How can RL optimize on-demand mobility?
A review on reinforcement learning methods for mobility on demand systems
January 7, 2025
https://arxiv.org/pdf/2501.02569This paper reviews how reinforcement learning (RL) is used to optimize Mobility on Demand (MoD) systems, such as ride-hailing services. It categorizes RL algorithms used for vehicle rebalancing, dispatch, and joint rebalancing/dispatch, analyzing their approaches and use cases.
Key points for LLM-based multi-agent systems:
- Sequential decision-making framework: The paper uses Powell's framework to categorize RL methods, including Policy Function Approximations (PFAs), Cost Function Approximations (CFAs), Value Function Approximations (VFAs), and Direct Lookahead Approximations (DLAs). This offers a structured way to think about LLM agent decision processes.
- Model-free RL's prominence: Most reviewed papers use model-free RL, learning directly from experience without explicit environment models. This is relevant to LLMs, which often operate in complex environments where creating accurate models is difficult.
- Decentralized control: Several studies explore decentralized RL, where each vehicle/agent learns its own policy, relevant to multi-agent LLM systems where agents might need to act autonomously.
- Focus on real-world data and simulators: Many papers use real-world datasets and simulators for evaluation, highlighting a trend toward practical applications, which is important for building deployable LLM agents.
- Transfer learning potential: One paper demonstrates the potential of transferring learned policies between different use cases (cities), suggesting a possible avenue for more efficient training of LLM agents in new environments.
- Challenges and future directions: The paper highlights the need for more benchmarks comparing different RL methods, the inclusion of public transport and heterogeneous vehicle fleets, and the potential of transfer learning. These are also relevant challenges for multi-agent LLM systems.