Practical overview
When it fits
Use Rl Tools when your work involves implementing or experimenting with deep reinforcement learning in C++, especially for continuous-control tasks or robotics-related prototypes.
Who it is for
- C++ developers implementing reinforcement-learning systems
- Robotics and control researchers prototyping learned policies
- Developers exploring inference on embedded platforms
Documented uses
- Train agents with SAC, TD3 or PPO on examples such as Pendulum, Acrobot, racing cars and MuJoCo Ant.
- Use the repository's multi-agent PPO example for a bottleneck environment.
- Build a custom environment, using the linked example project as a starting point.
- Explore documented inference paths for Crazyflie, ESP32 and PX4.
Requirements & limits
The repository is public and identifies the project as MIT-licensed.
- The examples cover SAC, TD3, PPO and multi-agent PPO across several control environments.
- The repository documents a C++17 build route and separately links C++ notebook documentation.
- Platform-specific inference references are listed for Crazyflie, ESP32 and PX4; iOS and Teensy are listed under training and inference.
- The README provides optional Accelerate instructions for macOS and OpenBLAS instructions for Ubuntu.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Build and visualize a Zoo training example
Compile the documented L2F SAC Zoo example and view its quadrotor visualization locally.
Before you start
- A C++17 compiler named g++
- A clone of the Rl Tools repository
- For the documented optional optimized backend: macOS with Accelerate, or Ubuntu with libopenblas-dev
Steps
From the repository, compile with
g++ -std=c++17 -O3 -ffast-math -I include src/rl/zoo/l2f/sac.cpp.Run the resulting executable with
./a.out 1337, using the number as the seed.Run
./tools/serve.sh, openhttp://localhost:8000, and navigate to the ExTrack UI.
Expected outcome
A locally served visualization of the quadrotor example.
Guide limitations
- The README gives platform-specific optional backend flags for macOS and Ubuntu; it does not document equivalent setup for other systems in this quick start.
Run the C++ documentation notebooks in Docker
Start the documented local environment for exploring the C++ notebooks.
Before you start
- Docker
Steps
Run the command below.
docker run -p 8888:8888 rltools/documentationOpen the link displayed in the command-line output in a browser.
Expected outcome
A browser-accessible local environment for tinkering with the documentation notebooks.
Guide limitations
- This guide covers launching the notebook environment, not building or training a custom agent.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 13 October 2026
- Original website title
- rl-tools/rl-tools · Official homepage title · retained in its source language