Addressing real-life issues before coding a solution

In this chapter, we are going to tackle Markov's Decision Process (Q function) and apply it to reinforcement learning with the Bellman equation. You can find tons of source code and examples on the web. However, most of them are toy experiments that have nothing to do with real life. For example, reinforcement learning can be applied to an e-commerce business delivery person, self-driving vehicle, or a drone. You will find a program that calculates a drone delivery. However, it has many limits that need to be overcome. You as an adaptive thinker are going to ask some questions:

• What if there are 5,000 drones over a major city at the same time?
• Is a drone-jam legal? What about the noise over the city? What about tourism?
• What about the weather? Weather forecasts are difficult to make, so how is this scheduled?

In just a few minutes, you will be at the center of attention, among theoreticians who know more than you on one side and angry managers who want solutions they cannot get on the other side. Your real-life approach will solve these problems.

A foolproof method is the practical three-dimensional approach:

• Be a subject matter expert (SME): First, you have to be an SME. If a theoretician geek comes up with a hundred Google DeepMind TensorFlow functions to solve a drone trajectory problem, you now know it is going to be a tough ride if real-life parameters are taken into account.
  An SME knows the subject and thus can quickly identify the critical factors of a given field. Artificial intelligence often requires finding a solution to a hard problem that even an expert in a given field cannot express mathematically. Machine learning sometimes means finding a solution to a problem that humans do not know how to explain. Deep learning, involving complex networks, solves even more difficult problems.
• Have enough mathematical knowledge to understand AI concepts: Once you have the proper natural language analysis,  you need to build your abstract representation quickly. The best way is to look around at your everyday life and make a mathematical model of it. Mathematics is not an option in AI, but a prerequisite. The effort is worthwhile. Then, you can start writing solid source code or start implementing a cloud platform ML solution.
• Know what source code is about as well as its potential and limits: MDP is an excellent way to go and start working in the three dimensions that will make you adaptive: describing what is around you in detail in words, translating that into mathematical representations, and then implementing the result in your source code.