Summary of Hierarchical Continual Reinforcement Learning Via Large Language Model, by Chaofan Pan et al.
Hierarchical Continual Reinforcement Learning via Large Language Model
by Chaofan Pan, Xin Yang, Hao Wang, Wei Wei, Tianrui Li
First submitted to arxiv on: 25 Jan 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 This paper proposes a novel framework for continual reinforcement learning (CRL), called Hierarchical Continual Reinforcement Learning via Large Language Model (Hi-Core). The framework aims to improve the transfer of high-level knowledge between tasks by employing a twolayer structure. The first layer involves a large language model that generates goals, while the second layer focuses on low-level policy learning. This approach enables the agent to adapt to new tasks and leverage previously learned skills. Experiments on Minigrid demonstrate the effectiveness of Hi-Core in handling diverse CRL tasks, outperforming popular baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Hi-Core is a new way for machines to learn from experience and apply it to new situations. It’s like having a library of skills that can be used again and again. The system uses two parts: one that comes up with goals and another that figures out how to achieve those goals. This helps the machine learn faster and make better decisions. The test results show that Hi-Core works well for complex tasks, even when they’re very different. |
Keywords
* Artificial intelligence * Large language model * Reinforcement learning
