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  • Name

    Francisco Soares Pinto
  • Role

    Research Assistant
  • Since

    01st April 2022


Decoding Reinforcement Learning for Newcomers

Neves, FS; Andrade, GA; Reis, MF; Aguiar, AP; Pinto, AM;


The Reinforcement Learning (RL) paradigm is showing promising results as a generic purpose framework for solving decision-making problems (e.g., robotics, games, finance). The aim of this work is to reduce the learning barriers and inspire young students, researchers and educators to use RL as an obvious tool to solve robotics problems. This paper provides an intelligible step-by-step RL problem formulation and the availability of an easy-to-use interactive simulator for students at various levels (e.g., undergraduate, bachelor, master, doctorate), researchers and educators. The interactive tool facilitates the familiarization with the key concepts of RL, its problem formulation and implementation. 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). Due to a lack of open-source graphical interactive simulators concerning the field of RL, this paper combines theoretical exposition with an accessible practical tool to facilitate the apprehension. The results demonstrated are produced by a Python script that is released as open-source to reduce the learning barriers in such innovative research topic in robotics.