Mc Ak Yerima

Aug 21, 2022

Reinforcement Learning

Reinforcement learning (RL) is an area of concerned with how ought to take in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside and .

Reinforcement learning differs from supervised learning in not needing labelled input/output pairs be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge). Partially supervised RL algorithms can combine the advantages of supervised and RL algorithms.

The environment is typically stated in the form of a (MDP), because many reinforcement learning algorithms for this context use techniques. 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.