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Machine and reinforcement learning (RL) are increasingly being applied toplan and control the behavior of autonomous systems interacting with thephysical world. Examples include self-driving vehicles, distributed sensornetworks, and agile robots. However, when machine learning is to be applied inthese new settings, the algorithms had better come with the same type ofreliability, robustness, and safety bounds that are hallmarks of controltheory, or failures could be catastrophic. Thus, as learning algorithms areincreasingly and more aggressively deployed in safety critical settings, it isimperative that control theorists join the conversation. The goal of thistutorial paper is to provide a starting point for control theorists wishing towork on learning related problems, by covering recent advances bridginglearning and control theory, and by placing these results within an appropriatehistorical context of system identification and adaptive control.

From self-tuning regulators to reinforcement learning and back again