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Sumo Rl

Reinforcement learning traffic signal control with sumo-rl, including SumoEnvironment setup, Gymnasium/PettingZoo APIs, observation/action/reward design, and training workflows. Use for RL signal control tasks or when SUMO-RL is requested.

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Sumo RL

Overview

Use this skill to design and run RL traffic signal control experiments with sumo-rl.

Skill Routing

  • Use sumo-env for installing SUMO or Python dependencies.
  • Use sumo-core to build networks, routes, and traffic light definitions.
  • Use sumo-mcp for automated RL training workflows or tool execution.
  • Use sumo-output for output analysis outside RL logs.

RL Workflow

  1. Prepare a network with traffic lights and a route file.
  2. Choose single-agent (Gymnasium) or multi-agent (PettingZoo) API.
  3. Select observation and reward functions.
  4. Train with your RL library or built-in examples.

Requirements

  • SUMO_HOME set and SUMO executables available.
  • Traffic lights must exist in the network for signal control.

References

  • Installation: references/rl-install.md
  • Observations, actions, rewards: references/rl-mdp.md
  • Gymnasium and PettingZoo APIs: references/rl-env-api.md

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