Summary of Efficient World Models with Context-aware Tokenization, by Vincent Micheli et al.
Efficient World Models with Context-Aware Tokenization
by Vincent Micheli, Eloi Alonso, François Fleuret
First submitted to arxiv on: 27 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 -IRIS agent combines a discrete autoencoder and an autoregressive transformer to predict future deltas in a world model, achieving state-of-the-art performance on the Crafter benchmark at multiple frame budgets while being faster to train than previous attention-based approaches. By leveraging recent advances in sequence modelling and generative modelling, this model-based RL method addresses the challenge of scaling up deep RL methods. The -IRIS architecture is well-suited for complex environments that require accurate simulation and efficient exploration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers developed a new agent called -IRIS to help computers learn from experience by playing games or solving problems. This agent uses a special type of model that helps it understand the world and make good decisions. The agent was tested on a game-playing task called Crafter, where it performed better than other agents while using less computer power. |
Keywords
» Artificial intelligence » Attention » Autoencoder » Autoregressive » Transformer
