A Comparative Study of Deep Reinforcement Learning Models: DQN vs PPO vs A2C

Autor:	De La Fuente, Neil, Guerra, Daniel A. Vidal
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	This study conducts a comparative analysis of three advanced Deep Reinforcement Learning models: Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Advantage Actor-Critic (A2C), within the BreakOut Atari game environment. Our research assesses the performance and effectiveness of these models in a controlled setting. Through rigorous experimentation, we examine each model's learning efficiency, strategy development, and adaptability under dynamic game conditions. The findings provide critical insights into the practical applications of these models in game-based learning environments and contribute to the broader understanding of their capabilities. The code is publicly available at github.com/Neilus03/DRL_comparative_study. Comment: 8 pages, Accepted at KDD 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.14151 Zobrazit plný text záznamu View this record from Arxiv