flow
A computational framework for deep reinforcement learning experiments in traffic microsimulation and control.
What is flow?
Flow is a computational framework for deep reinforcement learning and control experiments in traffic microsimulation. It provides tools to model mixed-autonomy traffic scenarios where autonomous and human-driven vehicles interact, enabling researchers to develop and benchmark RL algorithms for traffic optimization.
Researchers and academics working on reinforcement learning applications in transportation, traffic engineering, and autonomous vehicle control who need standardized environments for experimentation.
Flow offers a specialized, reproducible platform for traffic control research with built-in benchmarks and simulator integrations, making it easier to compare different RL approaches and advance the field of intelligent transportation systems.
Overview
Computational framework for reinforcement learning in traffic control
Use Cases
Best For
- Benchmarking reinforcement learning algorithms for traffic signal control
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