Wadaboa/flatland-challenge
Multi-agent reinforcement learning on trains, for Deep Learning class at UNIBO
This project provides solutions for optimizing train movements in a simulated railway network. It takes in railway network configurations and train movement rules, and outputs trained models that can efficiently manage multiple trains to reach their destinations with minimal delays and deadlocks. It's designed for researchers and students working on multi-agent reinforcement learning challenges in logistics and transportation.
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Use this if you are a researcher or student looking for implementations and approaches to solve multi-agent reinforcement learning problems, specifically in the context of railway traffic management.
Not ideal if you're looking for a ready-to-use application to manage a real-world train system or if you're not familiar with Python, deep learning frameworks like PyTorch, and reinforcement learning concepts.
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Jan 14, 2021
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