Названия:
DEVELOPING SNAKE AND RACING CAR ENVIRONMENTS FOR REINFORCEMENT LEARNING
Автор:
Gulov Gurbanberdi, Toylyyeva Ogulgozel, Myradov Rahman, Sapartach Hojabalkanova
Расположение страниц:
239-245
Язык:
Английский
Аннотация:
This article explores the development of interactive environments for Reinforcement Learning (RL) using two classic games: Snake and Racing Car. Both games offer distinct challenges and mechanics, making them ideal candidates for testing RL algorithms. The goal of this paper is to describe the process of designing and implementing these environments, integrating state-of-the-art RL techniques, and demonstrating the results of training agents to master these tasks. Key insights into the practical applications of RL in game development are provided, highlighting how RL agents can adapt and optimize strategies within dynamic, rule-based environments.