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Collab urban flow is a suite of multi agent reinforcement learning systems dedicated to adaptive traffic signal control

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Collaborative Urban Flow (CUF)

Is a set of reinforcement learning based adaptive traffic signal control systems.

Core Principles

  • Muh (minimalism):
    • Less frameworks as possible.
    • No. Reinforcement learning is not deep reinforcement learning.
    • The python should approximate a papers' pseudocode.
  • Data interfaces:
    • Data wrangling: preprocessing, saving, loading and transforming is certain.
    • For code consuming and producing data to behave in predictable ways. Data should have formal interfaces.
    • Hard data interfaces constrain the loose nature of Muh.
  • When in doubt follow UNIX:
    • Make it easy to write, test, and run programs.
    • Interactive use instead of batch processing.
    • Economy and elegance of design due to size constraints ("salvation through suffering").

Getting Started

  1. Install cityflow.
  2. Run:

    pip install -r requirements.txt.

  3. Invoke from the project's root directory:

    python jobs/run.py

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Collab urban flow is a suite of multi agent reinforcement learning systems dedicated to adaptive traffic signal control

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