alessandrositta/Flatland_challenge
Repository containing the code and explanation of a solution to the Flatland Challenge problem.
This project provides a solution to the Flatland Challenge, which involves optimizing train movement on a railway network. It takes in railway network layouts and train starting/ending points, and outputs efficient schedules for single or multiple trains to reach their destinations, even with malfunctions. This is useful for anyone interested in applying advanced AI techniques to complex scheduling and logistics problems.
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Use this if you are exploring reinforcement learning approaches for multi-agent scheduling challenges, particularly in a simulated railway environment.
Not ideal if you are looking for a ready-to-deploy, production-grade train scheduling system for real-world operations.
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Python
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Last pushed
Jul 28, 2020
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