India, April 8 -- ML algorithms allow us to model and predict Big Data behaviors based on historical data: looking back. But what if the historical database is not enough to model our problem? This is where the so-called reinforcement learning comes into account, and machines explore their environment while learning from scratch based on rewards and penalties. They are simply keeping a future vision towards a goal.

Supervised and unsupervised learning techniques are being applied to meet users, predict subscriber likes and behaviors, predict system failures, among other functions. In order to carry out such tasks, this type of algorithm usually requires a large amount of historical data, which records the different characteristics and po...