Summary of Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation, by Tian Xu et al.
Provably and Practically Efficient Adversarial Imitation Learning with General Function Approximation
by Tian Xu, Zhilong Zhang, Ruishuo Chen, Yihao Sun, Yang Yu
First submitted to arxiv on: 1 Nov 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 This research paper introduces a new method for adversarial imitation learning (AIL) with general function approximation, called optimization-based AIL (OPT-AIL). The authors explore the theoretical underpinnings of online AIL and prove that OPT-AIL achieves polynomial expert sample complexity and interaction complexity. This is significant because existing methods are primarily limited to simplified scenarios and involve complex algorithmic designs that hinder practical implementation. The new method centers on performing online optimization for reward functions and optimism-regularized Bellman error minimization for Q-value functions, requiring the approximate optimization of two objectives. Empirical studies demonstrate that OPT-AIL outperforms previous state-of-the-art deep AIL methods in several challenging tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to learn from an expert’s actions without actually being the expert. This is called adversarial imitation learning (AIL). Right now, there are some simple ways to do this, but they’re not very good at dealing with complex situations. The researchers came up with a new method that can handle more complicated scenarios and still works well. They tested it on several challenging tasks and found that it did better than other methods. |
Keywords
* Artificial intelligence * Optimization
