Summary of Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning, by Dylan J. Foster and Adam Block and Dipendra Misra
Is Behavior Cloning All You Need? Understanding Horizon in Imitation Learning
by Dylan J. Foster, Adam Block, Dipendra Misra
First submitted to arxiv on: 20 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Statistics Theory (math.ST); Machine Learning (stat.ML)
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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 paper explores imitation learning (IL) in sequential decision-making tasks. The goal is to mimic expert behavior by learning from demonstrations, with applications in robotics, autonomous driving, and autoregressive text generation. Behavior cloning (BC), a simple approach to IL, has limitations due to sample complexity, which motivates the development of online algorithms that can learn more efficiently. These algorithms aim to achieve linear horizon dependence under specific assumptions on the data and learner’s access to expert knowledge. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imitation learning helps machines learn from experts by watching them do a task. This is useful for things like self-driving cars or robots that need to mimic human behavior. One simple way to do this is called behavior cloning, but it has some limitations. The researchers are trying to find better ways to do imitation learning online, so they can learn more quickly and efficiently. |
Keywords
* Artificial intelligence * Autoregressive * Text generation
