RLtools
A high-performance, portable deep reinforcement learning library for continuous control, optimized for speed across CPUs, GPUs, and microcontrollers.
What is RLtools?
RLtools is a deep reinforcement learning library focused on continuous control problems, offering implementations of algorithms like TD3, SAC, and PPO. It solves the need for a fast, portable RL library that can train agents quickly on consumer hardware and deploy them on embedded devices. The library is optimized for performance across CPUs, GPUs, and microcontrollers.
Researchers and engineers working on deep reinforcement learning for robotics, control systems, and embedded AI who require high performance and deployment flexibility.
Developers choose RLtools for its exceptional speed benchmarks, cross-platform portability from servers to microcontrollers, and the ability to rapidly prototype and deploy RL agents without heavy infrastructure.
Overview
The Fastest Deep Reinforcement Learning Library
Use Cases
Best For
- Training RL agents quickly on laptop CPUs (e.g., MacBook M-series)
- Deploying trained policies to microcontrollers like ESP32 or Crazyflie drones
- Research in continuous control with MuJoCo or custom environments
- Educational purposes through interactive C++ notebooks and tutorials
- Multi-agent reinforcement learning scenarios
- High-performance inference on embedded systems with strict latency requirements
Not Ideal For
- Projects requiring extensive support for discrete action spaces or grid-based environments
- Teams exclusively using Python without C++ expertise or willingness to handle native compilation
- Applications needing out-of-the-box integration with a wide variety of simulation environments beyond MuJoCo and Gymnasium
Pros & Cons
Pros
Benchmarks show RLtools trains Pendulum with PPO and SAC significantly faster than other libraries on standard hardware, with optimizations for CPU (Accelerate/OpenBLAS) and GPU backends.
Provides microcontroller-optimized inference for devices like ESP32 and Crazyflie, enabling deployment on resource-constrained systems with high inference frequencies as shown in benchmarks.
Runs on macOS, Linux, Windows, iOS, and microcontrollers, with specific build flags for acceleration on different platforms, making it highly versatile for deployment.
Implements state-of-the-art algorithms like TD3, SAC, PPO, and Multi-Agent PPO with example environments for continuous control, supporting both research and real-world use.
Cons
The README explicitly states that using Python Gym environments 'can slow down the training significantly' compared to native RLtools environments, limiting ease for Python-centric workflows.
Primarily focused on continuous control tasks, with less emphasis on discrete action spaces or other RL domains, which may restrict its applicability for broader research.
Deploying on embedded platforms or building from source requires platform-specific knowledge and compilation steps, such as handling Accelerate on macOS or OpenBLAS on Linux.
Frequently Asked Questions
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