Summary of Dreaming Of Many Worlds: Learning Contextual World Models Aids Zero-shot Generalization, by Sai Prasanna et al.
Dreaming of Many Worlds: Learning Contextual World Models Aids Zero-Shot Generalization
by Sai Prasanna, Karim Farid, Raghu Rajan, André Biedenkapp
First submitted to arxiv on: 16 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 proposed contextual recurrent state-space model (cRSSM) tackles the challenge of zero-shot generalization to unseen dynamics in embodied agents. By incorporating context into Dreamer’s world model, cRSSM infuses the latent Markovian states with context information, enabling better inference and modeling of latent dynamics. The approach is evaluated on two CARL benchmark tasks, showing improved zero-shot generalization. Additionally, cRSSM disentangles the latent state from context, allowing Dreamer to extrapolate its “dreams” to unseen contexts. This achievement has implications for creating generally capable embodied agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Dreamer’s world model is used to create embodied agents that can perform tasks in various environments. The challenge is to make these agents work well in new situations they’ve never seen before. One way to solve this problem is by giving the agent more information about its environment, or “context.” This helps the agent learn how to behave in different situations. Researchers used a special type of model called a recurrent state-space model (RSSM) to help Dreamer’s world model understand context. They tested this approach on two tasks and found that it worked well. The agents were able to perform well in new situations without being trained specifically for those situations. |
Keywords
* Artificial intelligence * Generalization * Inference * Zero shot
