Reinforcement learning (RL) is an area of machine learning concerned with how software 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.

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).

The environment is typically stated in the form of a Markov decision process (MDP), because many reinforcement learning algorithms for this context utilize dynamic programming 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.

⭐️ Table of Content ⭐️
0:00 Reinforcement learning: basic algorithm
20:21 Reinforcement learning: Problem and varients


⭐️ Credit ⭐️
Prof. Laurenz Wiskott
Institut für Neuroinformatik
Ruhr-Universität Bochum, Germany, EU
License: Creative Commons Attribution license (reuse allowed)

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