Summary of Effective Exploration Based on the Structural Information Principles, by Xianghua Zeng et al.
Effective Exploration Based on the Structural Information Principles
by Xianghua Zeng, Hao Peng, Angsheng Li
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 paper proposes a novel framework for Reinforcement Learning called Structural Information-based Effective Exploration (SI2E). SI2E builds upon traditional information theory and incorporates structural mutual information to capture dynamics-relevant state-action representations. The framework minimizes structural entropy to derive an encoding tree, which is used to design an intrinsic reward mechanism that promotes enhanced coverage in the state-action space. Comprehensive evaluations on the MiniGrid, MetaWorld, and DeepMind Control Suite benchmarks show that SI2E outperforms state-of-the-art exploration baselines by up to 60.25% in terms of sample efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for robots to learn from their environment using something called Reinforcement Learning. It’s like learning from experience, but instead of trying many things and seeing what works, it uses math to figure out the best way to explore and find rewards. The new method is called SI2E, and it’s better than other methods at finding a good path through the environment and making sure it doesn’t get stuck in a rut. |
Keywords
* Artificial intelligence * Reinforcement learning
