Editing Machine learning (section)

=== Reinforcement learning ===
{{Main|Reinforcement learning}}
[[File:Reinforcement learning diagram.svg|right|frameless]]
Reinforcement learning is an area of machine learning concerned with how [[software agent]]s ought to take [[Action selection|actions]] in an environment so as to maximise some notion of cumulative reward. Due to its generality, the field is studied in many other disciplines, such as [[game theory]], [[control theory]], [[operations research]], [[information theory]], [[simulation-based optimisation]], [[multi-agent system]]s, [[swarm intelligence]], [[statistics]] and [[genetic algorithm]]s. In reinforcement learning, the environment is typically represented as a [[Markov decision process]] (MDP). Many reinforcement learning algorithms use [[dynamic programming]] techniques.<ref>{{Cite book|author1=van Otterlo, M.|author2=Wiering, M.|title=Reinforcement Learning |chapter=Reinforcement Learning and Markov Decision Processes |volume=12|pages=3–42 |year=2012 |doi=10.1007/978-3-642-27645-3_1|series=Adaptation, Learning, and Optimization|isbn=978-3-642-27644-6}}</ref> Reinforcement learning algorithms do not assume knowledge of an exact mathematical model of the MDP and are used when exact models are infeasible. Reinforcement learning algorithms are used in autonomous vehicles or in learning to play a game against a human opponent.