11 Facts About Reinforcement learning

Reinforcement learning is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward.

Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

The main difference between the classical dynamic programming methods and reinforcement learning algorithms is that the latter do not assume knowledge of an exact mathematical model of the MDP and they target large MDPs where exact methods become infeasible.

The problems of interest in reinforcement learning have been studied in the theory of optimal control, which is concerned mostly with the existence and characterization of optimal solutions, and algorithms for their exact computation, and less with learning or approximation, particularly in the absence of a mathematical model of the environment.

Purpose of reinforcement learning is for the agent to learn an optimal, or nearly-optimal, policy that maximizes the "reward function" or other user-provided reinforcement signal that accumulates from the immediate rewards.

The goal of a reinforcement learning agent is to learn a policy:, which maximizes the expected cumulative reward.

Thus, reinforcement learning is particularly well-suited to problems that include a long-term versus short-term reward trade-off.

Thanks to these two key components, reinforcement learning can be used in large environments in the following situations:.

Reinforcement learning requires clever exploration mechanisms; randomly selecting actions, without reference to an estimated probability distribution, shows poor performance.

Adversarial deep reinforcement learning is an active area of research in reinforcement learning focusing on vulnerabilities of learned policies.

Partially supervised approaches can alleviate the need for extensive training data in supervised Reinforcement learning while reducing the need for costly exhaustive random exploration in pure RL.