Reinforcement Learning | Machine Learning

Reinforcement Learning | Machine Learning

Reinforcement learning is a space of Machine Learning. It is tied in with making an appropriate move to amplify prize in a specific circumstance. It is utilized by different software and machines to observe the most ideal conduct or way it should take in a particular circumstance. 

Reinforcement learning varies from managed learning in a manner that in regulated learning the preparation information has the appropriate response key with it so the model is prepared with the right respond to itself while in reinforcement learning, there is no answer except for the reinforcement specialist chooses what to do to play out the given errand. Without even a trace of a preparation dataset, it will undoubtedly gain from its experience.

How it can be used for data stream mining?

Incremental learning, online learning, and data stream learning are terms commonly associated with learning algorithms that update their models given a continuous influx of data without performing multiple passes over data. Several works have been devoted to this area, either directly or indirectly as characteristics of big data processing, i.e., Velocity and Volume. Given the current industry needs, there are many challenges to be addressed before existing methods can be efficiently applied to real-world problems. In this work, we focus on elucidating the connections among the current stateof- the-art on related fields; and clarifying open challenges in both academia and industry. We treat with special care topics that were not thoroughly investigated in past position and survey papers. This work aims to evoke discussion and elucidate the current research opportunities, highlighting the relationship of different subareas and suggesting courses of action when possible.

Post a Comment

Previous Post Next Post