What is reinforcement learning in machine learning? We have discussed supervised and unsupervised learning before.
However, one of the main types of machine learning is reinforcement learning.
In short, it is in between these two types of machine learning processes.
As a result, reinforcement learning fills the gap between supervised and unsupervised learning.
Basically in supervised learning what have we seen? The algorithms train on the correct answers. On the contrary, in unsupervised learning, the algorithms train on similarities.
We have learned about clustering data.
In reinforcement learning we will see how the algorithms train on trial and error methods.
Let’s think of a concrete example.
In our childhood we learn to stand up and walk.
To do that, we try many times. We fall, stand up, hold something to rest and there are many strategies we need to adopt before we finally learn to stand up.
It’s a trial and error method, and it takes time to adjust with the environment. In between we try out many different strategies until we get the beat result.
Among these strategies, we always pick up some and discard more.
And this process keeps going on.
By doing this we learn how to improve the process of standing up. Right?
Consider another real life example. Search engine.
If you are a complete beginner your journey to learn TensorFlow might start from here.
For the TensorFlow beginners we have a dedicated category – TensorFlow for Beginners.
But besides that, you may need to learn several other machine learning and data science libraries.
As a result, you may check these categories as well – NumPy, Pandas, Matplotlib.
However, without learning Python, you cannot learn the usages of these libraries. Why? Because they all use Python as the Programming language.
Therefore please learn Python at the very beginning and start learning TensorFlow.
And, finally please check our Mathematics, Discrete Mathematics and Data Structures categories specially. We have tried to discuss from basic to intermediate level so that you can pick up the core ideas of TensorFlow.
Search is the fundamental part of any reinforcement learning.
Why? Because the algorithm searches over since the basis is possible inputs and outputs and it leads to maximizing the reward.
If we go back to the childhood experience, it makes more sense.
Standing up even for a fraction of seconds is a kind of reward.
As we progress we will learn reinforcement learning in machine learning in detail.
So stay tuned.
Leave a Reply