Summary of Imitation From Diverse Behaviors: Wasserstein Quality Diversity Imitation Learning with Single-step Archive Exploration, by Xingrui Yu et al.
Imitation from Diverse Behaviors: Wasserstein Quality Diversity Imitation Learning with Single-Step Archive Exploration
by Xingrui Yu, Zhenglin Wan, David Mark Bossens, Yueming Lyu, Qing Guo, Ivor W. Tsang
First submitted to arxiv on: 11 Nov 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 Wasserstein Quality Diversity Imitation Learning (WQDIL) addresses the challenge of learning diverse and high-performance behaviors from a limited set of demonstrations. Traditional imitation learning methods are designed to learn one specific behavior, even with multiple demonstrations, making them ineffective in this task. WQDIL improves the stability of imitation learning through latent adversarial training based on a Wasserstein Auto-Encoder (WAE) and mitigates behavior-overfitting using a measure-conditioned reward function with a single-step archive exploration bonus. The method outperforms state-of-the-art IL methods, achieving near-expert or beyond-expert performance on challenging continuous control tasks derived from MuJoCo environments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Learning is all about copying the best behaviors we see, but what happens when we only have a few examples to go by? Traditional copying techniques don’t do well in this situation. To solve this problem, scientists created a new way of learning called Wasserstein Quality Diversity Imitation Learning (WQDIL). This method helps us learn good behaviors from just a little practice. It does this by using two important ideas: first, it makes sure the copying process is stable and consistent; second, it prevents the copied behavior from becoming too specialized or repetitive. The results are impressive – WQDIL can copy complex behaviors with amazing accuracy. |
Keywords
» Artificial intelligence » Encoder » Overfitting
