This document discusses reinforcement learning and its history and applications. It introduces reinforcement learning as a machine learning method where an agent learns through trial and error using rewards and punishments from its environment. It notes that unlike supervised learning, reinforcement learning uses rewards rather than predefined correct outputs. The document also states that reinforcement learning will be significant for data science in 2019, though it has faced challenges in implementation and tools compared to other machine learning methods.

1. Nadar saraswathi college of arts and science,
Theni.
Big data analysis
REINFORCEMENT
BY
G.NIBIYA.,II-MSC(IT)

2. INTRODUCTION
Reinforcement Learning (RL) is a machine learning method that empowers a specialist to learn
in an intuitive environment by performing trial and error utilizing observations from its very own
activities and encounters.

3. HISTROY
In spite of the fact that both direct and reinforcement learning use mapping among input and
output, not at all like supervised learning where input gave to the specialist is basically the right
set of activities for playing out a task, reinforcement learning utilizes prizes and discipline as
signs for positive and negative conduct.
When compared with unsupervised learning, reinforcement learning is distinctive as far as
objectives are taken into consideration.
While the objective in unsupervised learning is to discover synonymities and contrasts between
data points, in reinforcement learning the objective is to locate a reasonable activity model that
would boost the aggregate total reward of the specialist.

4. DISCOVERY
Reinforcement learning will be a huge thing in Data science in 2019.
While RL has been around for quite a while in the scholarly world, it has barely observed any
industry adoption whatsoever.
Halfway on the grounds that there have been a lot of low-draping organic products to pick in
predictive analytics, yet for the most part as a result of the hindrances in execution, learning and
accessible tools.

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