Composability designed for Reinforcement Learning
Composability is an important property in computer programming, allowing to dynamically switch between program components during execution. machina was built and designed with this principle in mind, allowing for high flexibility on system and program development.
Specifically, the RL-policy interacts with the environment via generated trajectories, making the exchange of either component simple. For example, using machina, it is possible to switch between a simulated and a real-world environment during the training phase.