Summary of Omni-epic: Open-endedness Via Models Of Human Notions Of Interestingness with Environments Programmed in Code, by Maxence Faldor et al.
OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code
by Maxence Faldor, Jenny Zhang, Antoine Cully, Jeff Clune
First submitted to arxiv on: 24 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 paper introduces OMNI-EPIC, a novel framework that leverages foundation models to generate code specifying the next learnable and interesting tasks for AI systems. Building upon previous work in Open-endedness via Models of human Notions of Interestingness (OMNI), OMNI-EPIC combines environments programmed in code (EPIC) to create any simulatable learning task. The framework generates both environments (e.g., obstacle courses) and reward functions (e.g., progress through the course quickly without touching red objects). This allows OMNI-EPIC to adapt to reinforcement learning agents’ learning progress, generating tasks of suitable difficulty. The paper showcases the explosive creativity of OMNI-EPIC, continuously innovating to suggest new, interesting learning challenges. This development can propel self-improving AI systems and AI-generating algorithms. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to make artificial intelligence (AI) learn new things forever. Right now, AI is good at doing certain tasks, but it’s limited by what humans have programmed for it. The authors created a system called OMNI-EPIC that can generate its own learning challenges, making the AI smarter and more creative over time. This could lead to breakthroughs in many areas, such as playing games or helping people with important decisions. |
Keywords
» Artificial intelligence » Reinforcement learning
