Summary of A Comparative Study Of Deep Reinforcement Learning Models: Dqn Vs Ppo Vs A2c, by Neil De La Fuente and Daniel A. Vidal Guerra
A Comparative Study of Deep Reinforcement Learning Models: DQN vs PPO vs A2C
by Neil De La Fuente, Daniel A. Vidal Guerra
First submitted to arxiv on: 19 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This study compares the performance of three advanced Deep Reinforcement Learning (DRL) models – Deep Q-Networks (DQN), Proximal Policy Optimization (PPO), and Advantage Actor-Critic (A2C) – in the BreakOut Atari game environment. The researchers evaluate each model’s learning efficiency, strategy development, and adaptability under dynamic game conditions. The findings provide insights into the practical applications of these models in game-based learning environments and contribute to our understanding of their capabilities. The study uses DRL models, which are popular in fields like game theory and robotics, to analyze their strengths and weaknesses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study compares three different ways that computers can learn from experience. These methods are called Deep Reinforcement Learning (DRL) models. The researchers tested these models on a classic video game called BreakOut. They wanted to see how well each model could play the game and how it learned as it played. The results will help us understand when we should use which method for tasks like training robots or teaching computers to play games. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
