mlsanityData to 14 Oct 2026

SmartFlow Reinforcement Learning and Agentic AI for Bike-Sharing Optimisation

arXiv 2601.00868 ↗First posted 30 Dec 2025cs.LG cs.AIcited by 0

On the map: LLM agents and tool use → Reinforcement learning for LLM agents

Abstract (arXiv)

SmartFlow is a multi-layered framework that integrates Reinforcement Learning and Agentic AI to address the dynamic rebalancing problem in urban bike-sharing services. Its architecture separates strategic, tactical, and communication functions for clarity and scalability. At the strategic level, a Deep Q-Network (DQN) agent, trained in a high-fidelity simulation of New Yorks Citi Bike network, learns robust rebalancing policies by modelling the challenge as a Markov Decision Process. These high-level strategies feed into a deterministic tactical module that optimises multi-leg journeys and schedules just-in-time dispatches to minimise fleet travel. Evaluation across multiple seeded runs demonstrates SmartFlows high efficacy, reducing network imbalance by over 95% while requiring minimal travel distance and achieving strong truck utilisation. A communication layer, powered by a grounded Agentic AI with a Large Language Model (LLM), translates logistical plans into clear, actionable instructions for operational staff, ensuring interpretability and execution readiness. This integration bridges machine intelligence with human operations, offering a scalable solution that reduces idle time, improves bike availability, and lowers operational costs. SmartFlow provides a blueprint for interpretable, AI-driven logistics in complex urban mobility networks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reinforcement learning for LLM agents, one of 16 directions in LLM agents and tool use. Builds on 3 of its 24 references that are in the map. First posted on arXiv 30 Dec 2025.

Builds on

3 of 24 references.

References · 3

  1. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,695
  2. 2024Dual Policy Reinforcement Learning for Real-time Rebalancing in Bike-sharing Systemscited by 2
  3. 2020Towards Dynamic Urban Bike Usage Prediction for Station Network Reconfigurationcited by 1

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Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2024Single- vs. Dual-Policy Reinforcement Learning for Dynamic Bike Rebalancing
  2. 2026Fully Dynamic Rebalancing in Dockless Bike-Sharing Systems via Deep Reinforcement Learning
  3. 2025In-the-Flow Agentic System Optimization for Effective Planning and Tool Use
  4. 2026Human-Centric Traffic Signal Control for Equity: A Multi-Agent Action Branching Deep Reinforcement Learning Approach
  5. 2024Multi-Agent Deep Reinforcement Learning for Distributed and Autonomous Platoon Coordination via Speed-regulation over Large-scale Transportation Networks
  6. 2025LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models
  7. 2026AstraFlow: Dataflow-Oriented Reinforcement Learning for Agentic LLMs
  8. 2026SkillFlow: Flow-Driven Recursive Skill Evolution for Agentic Orchestration