Inverse Learning is implemented in order to learn a control/decision policy (in the integer space) from an Expert Agent. The Learner Agent assumes that the Expert is acting minimizing an unknown cost function and tries to approximate it, through its own parametrized version of it. Learning can be performed in two different ways: offline (exploiting a training set containing Expert data) and online, in which the Learner Agent is directly controlling the system while learning the policy, exploiting corrective advice from the Expert. We propose three different learning algorithms and draw a comparison between them, as well as assess their performance against the Expert. We use our Agent for two different applications: control of a dynamical system (1) and classic ML classification/regression tasks (2). For application (1), our case study is the Heavy Shell Oil Fractionator system, with an MPC Expert Agent. For application (2), we train and test our Agent on several real data-sets available online, with tasks such as medical diagnosis, social media comment volume prediction, fault detection/isolation and multi-class classification.