mlsanityData to 7 Oct 2026

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

arXiv 2407.14151 ↗First posted 19 Jul 2024cs.LGcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 6 of its 8 references that are in the map. First posted on arXiv 19 Jul 2024.

Builds on

6 of 8 references.

References · 6

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  3. 2016Asynchronous Methods for Deep Reinforcement Learningcited by 2,218
  4. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  5. 2015Dueling Network Architectures for Deep Reinforcement Learningcited by 769
  6. 2017Deep Reinforcement Learning that Matterscited by 727

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 5.

8 papers.

  1. 2025Context-Aware Adaptive Sampling for Intelligent Data Acquisition Systems Using DQNcited by 6
  2. 2025Automated Cyber Defense with Generalizable Graph-based Reinforcement Learning Agentscited by 6
  3. 2025Using Machine Learning to Take Stay-or-Go Decisions in Data-driven Drone Missionscited by 0
  4. 2026Component Centric Placement Using Deep Reinforcement Learningcited by 0
  5. 2026Safe reinforcement learning with online filtering for fatigue-predictive human-robot task planning and allocation in productioncited by 0
  6. 2026A hierarchical spatial-aware algorithm with efficient reinforcement learning for human-robot task planning and allocation in productioncited by 0
  7. 2026Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildingscited by 0
  8. 2026Trading Confidence: Comprehensive Uncertainty Estimation in Algorithmic Tradingcited by 0