LExCI: A Framework for Reinforcement Learning with Embedded Systems
Kevin Badalian Lucas Koch Tobias Brinkmann Mario Picerno Marius Wegener Sung-Yong Lee Jakob Andert
Advances in artificial intelligence (AI) have led to its application in many
areas of everyday life. In the context of control engineering, reinforcement
learning (RL) represents a particularly promising approach as it is centred
around the idea of allowing an agent to freely interact with its environment to
find an optimal strategy. One of the challenges professionals face when
training and deploying RL agents is that the latter often have to run on
dedicated embedded devices. This could be to integrate them into an existing
toolchain or to satisfy certain performance criteria like real-time
constraints. Conventional RL libraries, however, cannot be easily utilised in
conjunction with that kind of hardware. In this paper, we present a framework
named LExCI, the Learning and Experiencing Cycle Interface, which bridges this
gap and provides end-users with a free and open-source tool for training agents
on embedded systems using the open-source library RLlib. Its operability is
demonstrated with two state-of-the-art RL-algorithms and a rapid control
prototyping system.