Summary of Learning Generative Interactive Environments by Trained Agent Exploration, By Naser Kazemi et al.
Learning Generative Interactive Environments By Trained Agent Exploration
by Naser Kazemi, Nedko Savov, Danda Paudel, Luc Van Gool
First submitted to arxiv on: 10 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 The abstract discusses the limitations of Genie, a world model that excels at learning from visually diverse environments but relies on costly human-collected data. To improve this model, the authors propose employing reinforcement learning-based agents for data generation, which produces diverse datasets that enhance the model’s ability to adapt and perform well across various scenarios. The proposed approach is demonstrated through the release of GenieRedux, an implementation based on Genie, as well as a variant called GenieRedux-G that uses agent exploration to factor out action prediction uncertainty during validation. Evaluation results show that GenieRedux-G achieves superior visual fidelity and controllability using trained agent exploration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The abstract is about a way to improve the Genie model, which is used to understand and simulate complex environments. The current method of collecting data is expensive and limited. To fix this, researchers are trying to use agents that learn by doing things in the environment. This approach creates diverse datasets that help the model adapt to different situations. They tested this idea with a new version of Genie called GenieRedux-G and found it works better than before. |
Keywords
» Artificial intelligence » Reinforcement learning
