Summary of Effective Reinforcement Learning Based on Structural Information Principles, by Xianghua Zeng et al.
Effective Reinforcement Learning Based on Structural Information Principles
by Xianghua Zeng, Hao Peng, Dingli Su, Angsheng Li
First submitted to arxiv on: 15 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 Reinforcement Learning (RL) framework called SIDM is proposed to effectively make decisions in noisy and high-dimensional scenarios. The framework uses information-theoretic principles to extract abstract elements from historical trajectories, generating an optimal encoding tree that improves policy quality, stability, and efficiency. The approach introduces a two-layer skill-based learning mechanism that computes the common path entropy of each state transition, eliminating the need for expert knowledge. SIDM can be flexibly incorporated into various single-agent and multi-agent RL algorithms, enhancing their performance. This framework is demonstrated to significantly improve upon state-of-the-art baselines on challenging benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of using computers to learn from experience is presented in this paper. The system, called SIDM, helps a computer make good decisions by looking at patterns in what it’s done before. It uses special math ideas to find the best way to group things together and figure out what will happen next. This makes the computer’s decisions better, faster, and more consistent. SIDM can be used with many different kinds of computer learning systems, making them work even better. |
Keywords
» Artificial intelligence » Reinforcement learning
