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

2024-07-19 · Neil De La Fuente, Daniel A. Vidal Guerra

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.

📄 PDF Abstract BibTeX arXiv:2407.14151

Code (1)

neilus03/drl_comparative_study 공식 구현 pytorch

Tasks

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