Reinforcement Learning: Concepts and Practical Implementations

Abstract

As a general-purpose approach, the Reinforcement Learning (RL) concept of practice demonstrating encouraging results. Foundation for resolving issues with decision-making in fields including robotics, gaming, and finance. Reducing challenges to learning and encouraging educators, researchers, and young students to use reinforcement learning (RL) as a natural tool for robotics problem solving are the goals of this effort. This work offers researchers, educators, and students at different levels (undergraduate, bachelor, master, and doctoral) an understandable, step-by-step formulation of an RL problem as well as an accessible interactive simulator. The interactive tool helps users become familiar with the main ideas of reinforcement learning, as well as how to formulate and apply problems using it. In this work, RL is applied to a robotics 2D navigation problem where the robot must try to achieve a goal point while avoiding collisions with objects. For instructional purposes, a navigational problem is easy to understand and practical because there is just one possible outcome—that is, either the goal is accomplished or not, a collision occurs or not. Owing to the dearth of freely available graphical interactive simulators in the field of reinforcement learning, this work integrates theoretical explanation with a user-friendly practical tool to aid comprehension. The outcomes showcased are generated using an open-source Python script designed to lower the learning curve in this cutting-edge robotics research area.