Data is becoming the prime 21st century resource. Learning and processing all of this data surpasses human capability and capacity, meaning machines are unavoidable. Amongst the many machine learning paradigms, Reinforcement Learning is of especial interest; however, there is no information as to how the latter be included in process management, specifics and frameworks. Within the framework of this thesis, we researched and examined the theoretical basis for this paradigm, the various scenarios and problems, and tested and compared some of work environments, resulting in the paradigm’s inclusion in the area of processes control and optimisation, as well as providing an overview of machine learning in general. The bulk of this work presents the key building blocks and basis for the paradigm, focusing on its main algorithms and their characteristics. It also presents typical use scenarios and inherent problems within the paradigm itself. We present three public open-source libraries and one web-based service as examples of work and development environments. This thesis also presents guidelines and starting points for further study and research. Even though reinforced learning algorithms are slower when compared to other learning paradigms, they have a much wider scope of use and the potential to produce better autonomous learning machines.