Reinforcement Learning to Calculate Routes for Simulated Robot Safety Cones

Abstract

This paper presents a model of a cone using reinforcement learning, harnessing the self-learning capacity of Artificial Intelligence to improve process efficiency. The independent operation of the cone is achieved through a reward and punishment system based on approaching or reaching the goal. The cone must decide between 0° or 90° turns at each step to maximize long-term rewards. While the simulated robotic safety cones successfully reach their targets, the training process is time-consuming due to the numerous variables involved. Nonetheless, the rise of AI and its self-learning capabilities offer promising opportunities for process optimization.

Keywords

• reinforcement learning
• pathfinding
• simulation
• road environment
• work zone