Summary of Neuroplastic Expansion in Deep Reinforcement Learning, by Jiashun Liu and Johan Obando-ceron and Aaron Courville and Ling Pan
Neuroplastic Expansion in Deep Reinforcement Learning
by Jiashun Liu, Johan Obando-Ceron, Aaron Courville, Ling Pan
First submitted to arxiv on: 10 Oct 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 novel approach, Neuroplastic Expansion (NE), aims to address the fundamental challenge of loss of plasticity in learning agents. NE maintains learnability and adaptability throughout the entire training process by dynamically growing the network from a smaller initial size to its full dimension. The method consists of three key components: elastic topology generation based on potential gradients, dormant neuron pruning to optimize network expressivity, and neuron consolidation via experience review. Experimental results demonstrate that NE effectively mitigates plasticity loss and outperforms state-of-the-art methods across various tasks in MuJoCo and DeepMind Control Suite environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way for learning agents to adapt to changing situations. It’s like when our brains reorganize themselves as we learn new things. The approach, called Neuroplastic Expansion (NE), makes it possible for machines to keep learning and adapting even when the situation changes. NE works by growing the network of connections between neurons in a way that balances flexibility with stability. This allows machines to better handle complex, dynamic environments. |
Keywords
» Artificial intelligence » Pruning
