Summary of Imitating From Auxiliary Imperfect Demonstrations Via Adversarial Density Weighted Regression, by Ziqi Zhang et al.
Imitating from auxiliary imperfect demonstrations via Adversarial Density Weighted Regression
by Ziqi Zhang, Zifeng Zhuang, Jingzehua Xu, Yiyuan Yang, Yubo Huang, Donglin Wang, Shuai Zhang
First submitted to arxiv on: 28 May 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 Adversarial Density Regression (ADR) framework is a novel one-step supervised imitation learning algorithm that aims to correct the policy learned on unknown-quality data by utilizing demonstrations. ADR addresses limitations in previous IL algorithms, such as reliance on the Bellman operator and out-of-distribution state-actions. By fully integrating a density-weighted behavioral cloning objective with auxiliary imperfect demonstration, ADR can effectively align the distribution of policies trained on unknown-quality datasets with that of expert policies. Theoretical analysis demonstrates that minimizing ADR’s objective is akin to approaching the optimal value function. Experimental evaluations show that ADR outperforms selected IL algorithms in various domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to teach machines how to learn from imperfect data is proposed. This method, called Adversarial Density Regression (ADR), helps correct mistakes made by training on low-quality data. By using demonstrations of good behavior, ADR can make sure the machine learns the right thing. It’s better than other methods because it doesn’t rely on a specific way of updating information and it deals with situations where the machine makes mistakes. |
Keywords
» Artificial intelligence » Regression » Supervised
