开源多智能体车辆路径求解框架，支持多种复杂场景。
Multi-Agent Environments for Vehicle Routing Problems
- 基于PyTorch构建模块化框架，统一支持经典、动态等四类问题
- 采用AEC游戏模型设计，提供易用API便于快速集成
- 助力强化学习与运筹学领域协同创新
强化学习（RL）在离散优化问题中的研究日益增多，正拓展至传统上由运筹学（OR）主导的领域。车辆路径问题（VRP）是具有高实际意义的典型离散优化问题，近年来在强化学习方面取得显著进展。然而，当前开源开发框架仍十分稀缺，阻碍了算法测试与结果客观比较，制约了领域进展，并限制了强化学习与运筹学社区间的交流。为此，本文提出MAEnvs4VRP库，一个统一的多智能体车辆路径环境框架，支持经典、动态、随机及多任务问题变体于同一模块化设计中。该框架基于PyTorch构建，具备灵活可定制的架构，便于引入新路由问题。其遵循智能体-环境周期（AEC）游戏模型，配备直观的API，支持快速采用并无缝集成至现有强化学习框架。项目源码可在https://github.com/ricgama/maenvs4vrp获取。
原文摘要 · Abstract (English)
Research on Reinforcement Learning (RL) approaches for discrete optimization problems has increased considerably, extending RL to areas classically dominated by Operations Research (OR). Vehicle routing problems are a good example of discrete optimization problems with high practical relevance, for which RL techniques have achieved notable success. Despite these advances, open-source development frameworks remain scarce, hindering both algorithm testing and objective comparison of results. This situation ultimately slows down progress in the field and limits the exchange of ideas between the RL and OR communities. Here, we propose MAEnvs4VRP library, a unified framework for multi-agent vehicle routing environments that supports classical, dynamic, stochastic, and multi-task problem variants within a single modular design. The library, built on PyTorch, provides a flexible and modular architecture design that facilitates customization and the incorporation of new routing problems. It follows the Agent Environment Cycle ("AEC") games model and features an intuitive API, enabling rapid adoption and seamless integration into existing reinforcement learning frameworks. The project source code can be found at https://github.com/ricgama/maenvs4vrp.
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