Definitions for reinforcement learning
re·in·force·ment learn·ing

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1. Reinforcement learning

   Reinforcement learning (RL) 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. 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 Markov decision process (MDP), because many reinforcement learning algorithms for this context use 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.

How to pronounce reinforcement learning?

1. Alex

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How to say reinforcement learning in sign language?

Numerology

1. Chaldean Numerology

   The numerical value of reinforcement learning in Chaldean Numerology is: 9

2. Pythagorean Numerology

   The numerical value of reinforcement learning in Pythagorean Numerology is: 9

References

1. ^ Wikipedia
   https://en.wikipedia.org/wiki/Reinforcement_Learning