A Comparative Study of Deep Reinforcement Learning Models: DQN vs PPO vs A2C
It provides insights for researchers and practitioners in game-based learning, but is incremental as it compares existing methods without introducing new techniques.
This study compared the performance of DQN, PPO, and A2C deep reinforcement learning models in the BreakOut Atari game, analyzing their learning efficiency and adaptability, but did not report specific numerical results.
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.