From Atari to Industry: the Road to Applied Reinforcement Learning

Game-based environments have been the bread and butter of RL research for decades. Games can be designed or chosen based on the complexity researchers want to tackle and results are easy to benchmark thanks to open-source efforts like Open AI’s gym standardizing the environment – agent interface. This standardization has been a catalyst for a thriving open-source community that has produced amazing tools like RLlib, which holds a wealth of high-quality state-of-the-art agent implementations. Thanks to these developments the stars are finally aligned for RL to move beyond the world of games. To get started, you no longer have to code agents from scratch, something notoriously hard to do and debug. Instead, you can focus on creating high-quality environments that are digital twins of real-world systems, and clever reward functions that guide your agents to greatness.