Reinforcement Learning Research Field

A Reinforcement Learning Research Field is a branch of the Machine Learning Discipline that is concerned with how software agents optimize actions in an given environment by maximizing a cumulative reward function.

• AKA: Reinforcement Learning Discipline.
• See: John Tsitsiklis, Machine Learning, Software Agent, Action Selection, Supervised Learning, Unsupervised Learning, Markov Decision Process, Dynamic Programming, Dimitri Bertsekas.

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References

2019

• (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Reinforcement_learning Retrieved:2019-5-9.
  • Reinforcement learning (RL) is an area of machine learning concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Reinforcement learning is considered as one of three machine learning paradigms, alongside supervised learning and unsupervised learning.

    It differs from supervised learning in that labelled input/output pairs need not be presented, and sub-optimal actions need not be explicitly corrected. Instead the focus is finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge).

    The environment is typically formulated as a Markov decision process (MDP), as 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.

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