Summary of Transforming Game Play: a Comparative Study Of Dcqn and Dtqn Architectures in Reinforcement Learning, by William A. Stigall
Transforming Game Play: A Comparative Study of DCQN and DTQN Architectures in Reinforcement Learning
by William A. Stigall
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 Deep Q-Networks (DQNs) incorporating Convolutional Neural Networks (CNNs) and Transformer architectures across three Atari games: Asteroids, Space Invaders, and Centipede. The authors investigate the capabilities of CNN-based DQNs, which have been extensively studied, and Transformer-based DQNs, a relatively unexplored area. The findings indicate that in the 35-40 million parameter range, the CNN-based DQN (DCQN) outperforms the Transformer-based DQN (DTQN) in speed, with the DCQN excelling across all games except Centipede. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study compares different types of computer programs that learn to play video games. It looks at two types: one based on a type of AI called Convolutional Neural Networks and another based on something called Transformers. The researchers test these programs on three classic video games, Asteroids, Space Invaders, and Centipede. They found that the program using Convolutional Neural Networks is faster than the Transformer-based program at playing most of the games. |
Keywords
» Artificial intelligence » Cnn » Transformer
