Reinforcement Learning and Adaptive Control

Abstract

Reinforcement learning (RL) is a machine learning paradigm in which an agent attempts to learn a control policy that can generate the desired sequence of actions for achieving a higher level objective. RL promises to provide a learning mechanism via which autonomous agents can learn to control themselves directly through experience, without requiring manual coding of control policies. Similar to other machine learning paradigms, RL research heavily focuses on end-to-end learning, which in this case is learning of policies directly through experience. Recent successes of RL have shown that agents can learn to decision making and control policies on complex simulations for which it would have been very difficult to manually create control policies. Some examples include chess, go, and more recently complex continuous timesimulated domains. Some pressing issues include sample complexity, robustness, and reliable simulations to the real-world transfer.