In reinforcement learning, this is an area of machine learning, it is about taking suitable action 1
to maximize reward in a particular situation, it is employed by various software and machines to find 2
the best possible behavior or path it should take in a specific situation. 3
We can also say that reinforcement learning differs from supervised learning in a way that, in the supervised 4
learning. 5
The training data has the answer key with it, so the model is trained with the correct answer itself. 6
But for the reinforcement learning, there is no answer. 7
Enforcement agent decides what to do to perform the given task, meaning the machine itself find a way 8
to solve a particular solution by receiving rewards. 9
Here we have the environment and we have an agent. First, 10
this agent needs to take some action to observe the area. If it just goes straight, 11
there is no reward! 12
So first it would be observation, after just observed, 13
there are some internal state and based on these learning processes, it will take some action if 14
it goes in this example to the right side, again there is no reward. 15
But if it goes to the backward and then turns left, then there is a reward, it will receive a reward. 16
So meaning this is a correct path! 17
It will goes its way until it finally satisfied a system and achieve the goal.