Summary of Enabling Adaptive Agent Training in Open-ended Simulators by Targeting Diversity, By Robby Costales and Stefanos Nikolaidis
Enabling Adaptive Agent Training in Open-Ended Simulators by Targeting Diversity
by Robby Costales, Stefanos Nikolaidis
First submitted to arxiv on: 7 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO); Machine Learning (stat.ML)
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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 method, called DIVA, presents an evolutionary approach for generating diverse training tasks in complex, open-ended simulators. This method aims to overcome the bottleneck of relying on a superabundance of training data representative of the target domain. By incorporating realistically-available domain knowledge and exploiting the structure embedded in well-designed simulators, DIVA can be applied to arbitrary parameterizations and outperforms competitive baselines from prior literature. The results showcase the potential of semi-supervised environment design (SSED) approaches to enable training in realistic simulated domains and produce more robust and capable adaptive agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to make AI learn faster by generating many different scenarios for it to practice on. Right now, making AI learn requires tons of data, which can be hard to collect. The authors propose an evolutionary approach called DIVA that can create diverse training tasks in complex simulators. This means AI agents can train better and adapt more quickly. The results show that DIVA outperforms other methods and has the potential to make AI more robust and capable. |
Keywords
» Artificial intelligence » Semi supervised
