RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control
This work addresses the problem of deploying efficient RL on embedded devices for real-world applications, introducing a novel library with broad implications for TinyRL.
The authors tackled the slow training times and lack of portability in deep reinforcement learning for continuous control by developing RLtools, a fast, dependency-free C++ library that solves RL problems up to 76 times faster than other frameworks and enables training directly on microcontrollers.
Deep Reinforcement Learning (RL) can yield capable agents and control policies in several domains but is commonly plagued by prohibitively long training times. Additionally, in the case of continuous control problems, the applicability of learned policies on real-world embedded devices is limited due to the lack of real-time guarantees and portability of existing libraries. To address these challenges, we present RLtools, a dependency-free, header-only, pure C++ library for deep supervised and reinforcement learning. Its novel architecture allows RLtools to be used on a wide variety of platforms, from HPC clusters over workstations and laptops to smartphones, smartwatches, and microcontrollers. Specifically, due to the tight integration of the RL algorithms with simulation environments, RLtools can solve popular RL problems up to 76 times faster than other popular RL frameworks. We also benchmark the inference on a diverse set of microcontrollers and show that in most cases our optimized implementation is by far the fastest. Finally, RLtools enables the first-ever demonstration of training a deep RL algorithm directly on a microcontroller, giving rise to the field of TinyRL. The source code as well as documentation and live demos are available through our project page at https://rl.tools.