  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.
