An intelligible step-by-step Reinforcement Learning (RL) problem formulation and the availability of an easy-to-use demonstrative toolbox for students at various levels (e.g., undergraduate, bachelor, master, doctorate), researchers and educators. This tool facilitates the familiarization with the key concepts of RL, its problem formulation and implementation. The results demonstrated in this paper are produced by a Python program that is released open-source, along with other lecture materials to reduce the learning barriers in such innovative research topic in robotics. The RL paradigm is showing promising results as a generic purpose framework for solving decision-making problems (e.g., robotics, games, finance). In this work, RL is used for solving a robotics 2D navigational problem where the robot needs to avoid collisions with obstacles while aiming to reach a goal point. A navigational problem is simple and convenient for educational purposes, since the outcome is unambiguous (e.g., the goal is reached or not, a collision happened or not). Thus, the intent is to accelerate the adoption of RL techniques in the field of mobile robotics. Motivate and promote the adoption of RL techniques to solve decision-making problems, specifically in robotics. Due to a lack of accessible educational and demonstrative toolboxes concerning the field of RL, this work combines theoretical exposition with an accessible open-source graphical interactive toolbox to facilitate the apprehension. This study aims to reduce the learning barriers and inspire young students, researchers and educators to use RL as an obvious tool to solve robotics problems.