Summary of Self-evolving Autoencoder Embedded Q-network, by J. Senthilnath et al.
Self-evolving Autoencoder Embedded Q-Network
by J. Senthilnath, Bangjian Zhou, Zhen Wei Ng, Deeksha Aggarwal, Rajdeep Dutta, Ji Wei Yoon, Aye Phyu Phyu Aung, Keyu Wu, Min Wu, Xiaoli Li
First submitted to arxiv on: 18 Feb 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 A novel approach to reinforcement learning (RL) is proposed, which combines a self-evolving autoencoder (SA) with a Q-Network (QN) to enhance exploration capabilities. The SA adapts and evolves as the agent interacts with the environment, capturing diverse raw observations and representing them effectively in its latent space. By leveraging disentangled states from this space, the QN is trained to determine optimal actions that improve rewards. A bias-variance regulatory strategy is employed during autoencoder evolution, fostering growth of nodes while pruning least contributing ones to maintain a manageable representation. Experimental evaluations on three benchmark environments and a real-world molecular environment show SAQN outperforms state-of-the-art counterparts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers create a new way for artificial intelligence (AI) to learn and make decisions. They combine two important AI tools: an autoencoder that helps the AI understand its surroundings, and a Q-Network that decides what actions to take. The autoencoder is special because it changes and improves as the AI interacts with its environment. This helps the AI learn more quickly and make better choices. The team tested their approach on several different tasks and found that it worked much better than other methods. |
Keywords
* Artificial intelligence * Autoencoder * Latent space * Pruning * Reinforcement learning
