Machine learning versus traditional applications

Reinforcement learning based on stochastic (random) processes will evolve beyond traditional approaches. In the past, we would sit down and listen to future users to understand their way of thinking.

We would then go back to our keyboard and try to imitate the human way of thinking. Those days are over. We need proper datasets and ML/DL equations to move forward. Applied mathematics has taken reinforcement learning to the next level. Traditional software will soon be in the museum of computer science. 

An artificial adaptive thinker sees the world through applied mathematics translated into machine representations.

Use the Python source code example provided in this chapter in different ways. Run it; try to change some parameters to see what happens. Play around with the number of iterations as well. Lower the number from 50,000 down to where you find its best. Change the reward matrix a little to see what happens. Design your own reward matrix trajectory. It can be an itinerary or a decision-making process.