Summary of Language-guided Skill Learning with Temporal Variational Inference, by Haotian Fu et al.
Language-guided Skill Learning with Temporal Variational Inference
by Haotian Fu, Pratyusha Sharma, Elias Stengel-Eskin, George Konidaris, Nicolas Le Roux, Marc-Alexandre Côté, Xingdi Yuan
First submitted to arxiv on: 26 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 algorithm discovers reusable skills from expert demonstrations by combining Large Language Models (LLMs) with a hierarchical variational inference framework. The method initially segments trajectories using LLMs and then merges segments to discover skills that can be used to accelerate learning on new tasks. The skill discovery process is guided by an auxiliary objective based on the Minimum Description Length principle, which controls the trade-off between compression and reusability. Experiments demonstrate that agents equipped with this algorithm outperform baseline approaches in BabyAI and ALFRED environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a way to learn skills from watching experts do things. It’s like taking a cooking class and then being able to make your own meals later. The method uses big language models to help break down the actions into smaller parts, and then combines those parts into reusable skills. This helps agents learn new tasks faster. The researchers tested this on two environments: one where an AI has to navigate a grid world, and another where it has to complete household chores. Their results show that their method is better than previous approaches. |
Keywords
* Artificial intelligence * Inference
