Summary of Diffusion Imitation From Observation, by Bo-ruei Huang et al.
Diffusion Imitation from Observation
by Bo-Ruei Huang, Chun-Kai Yang, Chun-Mao Lai, Dai-Jie Wu, Shao-Hua Sun
First submitted to arxiv on: 7 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 Diffusion Imitation from Observation (DIFO) framework integrates a diffusion model into the adversarial imitation learning from observation framework to learn expert behavior without requiring action labels. By generating next states given current states, the diffusion model captures expert and agent transitions, which are then used to provide “realness” rewards for policy learning. This approach demonstrates superior performance in various continuous control domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Learning from observation (LfO) is a way to imitate experts by watching what they do without needing specific instructions. A new method called DIFO helps make this process better by using a special type of model that can generate future states given the current state. This allows for more realistic imitation and can be used in various situations, such as navigation or games. |
Keywords
» Artificial intelligence » Diffusion » Diffusion model
