Reinforcement Learning (RL) – 3rd / last post in this sub series “Machine Learning Type” under master series “Machine Learning Explained“. Next sub series “Machine Learning Algorithms Demystified” coming up. This post talks about reinforcement machine learning only.
RL compared with a scenario like “how some new born baby animals learns to stand, run, and survive in the given environment.”
Some Basics – Reinforcement Learning
Reinforcement Learning (RL) is more general than supervised learning or unsupervised learning. It learn from interaction with environment to achieve a goal or simply learns from reward and punishments. In other words algorithms learns to react to the environment. TD-learning seems to be closest to how humans learn in this type of situation, but Q-learning and others also have their own advantages.
Reinforcement learning can be referred to a learning problem and a subfield of machine learning at the same time. As a learning problem, it refers to learning to control a system so as to maximize some numerical value which represents a long-term objective.
Although RL has been around for many years as the third pillar for Machine Learning and now becoming increasingly important for Data Scientist to know when and how to implement. RL getting importance and focus as an equally important player with other two machine learning types reflects it rising importance in AI.
RL has some goals mentioned as below.
- Decision Process
- Reward/Penalty System
- Recommendation System
What is Reinforcement Learning
Before we get into deeper in RL for what and why, lets find out some history of RL on how it got originated. From the best research I got the answer as it got termed in 1980’s while some research study was conducted on animals behaviour. Especially how some new born baby animals learns to stand, run, and survive in the given environment. Rewards is a survival from learning and punishment can be compared with being eaten by others.
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