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

Neil De La Fuente, Daniel A. Vidal Guerra
7/19/2024
cs.LG

No official code found. Best implementation: Neilus03/DRL_comparative_study (12 stars, last commit 2024-05-27).

Abstract

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.

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Neilus03/DRL_comparative_studyCommunity100%
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Cite this paper

@article{fuente2024comparative,
  title  = {A Comparative Study of Deep Reinforcement Learning Models: DQN vs PPO vs A2C},
  author = {Neil De La Fuente and Daniel A. Vidal Guerra},
  year   = {2024},
  eprint = {2407.14151},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2407.14151v1}
}

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