flatland-association/flatland-rl
The Flatland Framework is a multi-purpose environment to tackle problems around resilient resource allocation under uncertainty. It is designed to be a flexible and method agnostic to solve a wide range of problems in the field of operations research and reinforcement learning.
Flatland helps operations research and logistics professionals develop and compare multi-agent decision-making systems for complex scheduling and resource allocation problems, like managing train movements. It takes a problem definition in a grid-based environment and outputs simulations and insights into how different algorithms perform. This is for researchers and engineers working on optimization in transport and similar fields.
Available on PyPI.
Use this if you need a flexible environment to test and refine algorithms for coordinating multiple autonomous agents in scenarios with shared resources and potential conflicts.
Not ideal if you are looking for a ready-to-use, off-the-shelf solution for a specific logistics problem without needing to develop custom algorithms.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Mar 13, 2026
Commits (30d)
0
Dependencies
31
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