Introduction to Reinforcement Learning: Learning from Experience| Dileenet A/L ICT

We talked about supervised learning and unsupervised learning in previous posts. Today's post will talk about reinforcement learning.

 In reinforcement learning, the model is trained from its own experiences or from what it has learned.

 Let's look at an example

 In the image below, there is a cow. There is grass. Imagine that this cow wants to eat this grass.

 This cow tries to follow the blue arrow path to eat grass. When it does so, it burns. Then the cow learns that it cannot go on fire while it is burning. Then it chooses another path to go. It tries to follow the orange arrow path. But it sees fire. Then it does not go through it because it has previous experience. Instead, it goes through the green arrow from the bottom box to where there is grass. This cow made decisions based on what it has learned.

With the example we discussed, we can demonstrate 

Reinforcement Learning with the simple diagram shown below.

According to this diagram, the cow can be considered as an AGENT. 

Then, according to each action that the cow takes, that is, according 

to the boxes it tries to go, it gets rewards from the environment. If it 

gets a negative reward, that is, if it tries to go through the fire, it 

learns from there and does not do that again. Similarly, if it gets a 

positive reward, it learns from that too. Then it takes different 

actions again with what it has learned. This is how a simple 

Reinforcement algorithm works.

 You can use

 Reinforcement Learning Techniques

•  Markov decision process (MDP)
•  Bellman equation.
•  Dynamic programming.
•  Value iteration.
•  Policy iteration.
•  Q-learning.

 It can be called.

 Let's see Reinforcement Learning Application

•  Natural Language Processing
•  Health care
•  Robotics and industries

 Then we will talk about Linear Regression in the next POST.

 If you have any questions, please comment.

Previous Post Link

https://e-learnict.blogspot.com/2026/01/dogs-cats-and-data-understanding.html

https://e-learnict.blogspot.com/2026/01/machine-learning-supervised-vs.html

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