Summary of Intelligent Go-explore: Standing on the Shoulders Of Giant Foundation Models, by Cong Lu et al.
Intelligent Go-Explore: Standing on the Shoulders of Giant Foundation Models
by Cong Lu, Shengran Hu, Jeff Clune
First submitted to arxiv on: 24 May 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 Intelligent Go-Explore (IGE) algorithm builds upon the original Go-Explore approach by leveraging giant pretrained foundation models (FMs) to replace manually designed heuristics for guiding exploration. This enables IGE to identify promising states and recognize serendipitous discoveries, even in complex environments where heuristics are hard to define. The algorithm is evaluated on a range of language and vision-based tasks that require search and exploration, exceeding classic reinforcement learning and graph search baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary IGE is an innovative algorithm that helps computers explore new ideas and discover interesting things. It uses powerful models trained on huge amounts of data to figure out what’s important and what’s not. This allows IGE to find great solutions even when it doesn’t know exactly what to look for. The algorithm is tested on various tasks, like playing games or recognizing objects, and performs much better than other methods. |
Keywords
» Artificial intelligence » Reinforcement learning
