Summary of Reward-free World Models For Online Imitation Learning, by Shangzhe Li et al.
Reward-free World Models for Online Imitation Learning
by Shangzhe Li, Zhiao Huang, Hao Su
First submitted to arxiv on: 17 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 proposed online imitation learning approach leverages reward-free world models to efficiently model complex tasks with high-dimensional inputs and dynamics. By formulating the optimization process in the Q-policy space using inverse soft-Q learning, the method mitigates instability issues. The approach consistently achieves expert-level performance on benchmarks like DMControl, MyoSuite, and ManiSkill2. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how machines can learn new skills just by watching experts do those tasks. It’s called imitation learning. But it’s hard to make this work when the tasks are very complex or have lots of information. The researchers came up with a new way to make this easier, using something called reward-free world models. This lets them create a model of how things work in the world without needing rewards or punishments. They used this approach to help machines learn to do tasks like controlling robots and playing games. |
Keywords
* Artificial intelligence * Optimization
