Automation via Reinforcement Learning

The dream of reinforcement learning is that it can one day be used to derive automated solutions to real-world tasks, with little-to-no human effort1. Unfortunately, in its current state, RL fails to deliver. There have been basically no real-world problems solved by DRL; even on toy problems, the solutions found are often brittle and fail to generalize to new environments. This means that the per-task human effort – i.e. task-specific engineering effort and hyperparameter tuning – is quite high. Algorithms are sample-inefficient, making them expensive in terms of both data collection effort and compute effort, too.