Summary of Fdqn: a Flexible Deep Q-network Framework For Game Automation, by Prabhath Reddy Gujavarthy
FDQN: A Flexible Deep Q-Network Framework for Game Automation
by Prabhath Reddy Gujavarthy
First submitted to arxiv on: 29 May 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 The proposed Flexible Deep Q-Network (FDQN) framework addresses the challenge of automating high-dimensional, rapid decision-making in dynamic environments. The self-adaptive approach utilizes a convolutional neural network (CNN) to process sensory data in real-time, dynamically adapting the model architecture to varying action spaces across different gaming environments. This outperforms previous baseline models in various Atari games and the Chrome Dino game using the epsilon-greedy policy, effectively balancing new learning and exploitation for improved performance. The modular structure allows for easy adaptation to other HTML-based games without modifying the core framework. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The FDQN framework helps computers make better decisions quickly in changing situations. It uses a special kind of artificial intelligence called a convolutional neural network (CNN) that can process lots of information in real-time. This allows it to adapt to different game environments and make smart moves. The framework does this by balancing learning new things and using what it already knows, which helps it win games like the Chrome Dino game. This technology could be used for more complex tasks in the future. |
Keywords
» Artificial intelligence » Cnn » Neural network
