Sumo-Rl

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sumo-rl provides Reinforcement Learning environments for Traffic Signal Control with SUMO. It offers a simple interface compatible with Gymnasium, PettingZoo, and popular RL libraries for developing and testing AI agents.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Yes

Skill level

Technical

About

What is sumo-rl?

sumo-rl is an open-source tool designed to simplify the creation and management of Reinforcement Learning (RL) environments for Traffic Signal Control using SUMO. It offers a straightforward interface, ensuring compatibility with widely used RL libraries and frameworks such as Gymnasium, PettingZoo, stable-baselines3, and RLlib. The tool supports both single-agent and multi-agent RL scenarios, allowing for flexible experimentation. Users can easily customize observation spaces and reward functions to suit their specific research or application needs. sumo-rl is particularly useful for developers and researchers focused on advancing AI agents for traffic management and optimization, providing a robust platform for simulating and evaluating different control strategies.

Best used for

Ideal for developers and data scientists who need to simulate complex traffic scenarios, develop and test reinforcement learning agents for traffic signal control, and conduct research on multi-agent traffic optimization. Especially valuable for those working with SUMO and popular RL libraries like Gymnasium and PettingZoo.

Common actions

simulate traffic

develop RL agents

control traffic signals

research AI algorithms

workflowsopen-sourceautomated workflowface swappingcollaborationgithub copilotlow-code/no-codedeepfake"AI Agents"

Capabilities

Key features

SUMO RL environments
Gymnasium compatibility
PettingZoo compatibility
Customizable observations
Customizable rewards
Multi-agent support

Target Audience

developerdata scientistprofessor

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What are the system requirements for installing sumo-rl?

To install sumo-rl, you first need to install SUMO (Simulation of Urban MObility) latest version. The documentation provides commands for adding the SUMO repository and installing sumo, sumo-tools, and sumo-doc on Ubuntu-based systems. You also need to set the SUMO_HOME environment variable.

Can I use sumo-rl for both single-agent and multi-agent reinforcement learning?

Yes, sumo-rl supports both single-agent and multi-agent environments. For single-agent scenarios, you can instantiate it as a standard Gymnasium Env. For multi-agent environments, it provides PettingZoo compatibility with AEC or Parallel API, allowing for complex multi-agent simulations.

How can I customize the observation and reward functions in sumo-rl?

sumo-rl allows for easy customization of observation and reward functions. You can define your own observation by implementing a class that inherits from ObservationFunction. Similarly, you can choose from implemented reward functions or create your own custom reward function by passing it to the SumoEnvironment constructor.

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Coding & Development › Open Source & Models Research & Education › Scientific Computing Productivity & Business › Workflow Automation

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