Summary of A Survey Of Imitation Learning Methods, Environments and Metrics, by Nathan Gavenski et al.
A Survey of Imitation Learning Methods, Environments and Metrics
by Nathan Gavenski, Felipe Meneguzzi, Michael Luck, Odinaldo Rodrigues
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 abstract presents a survey of the field of imitation learning, where an agent learns to perform a task by mimicking one or more teachers. The approach balances learning from teachers with deviating when necessary. Despite its popularity, the field lacks standardization in environments and evaluation processes. To address this, the paper introduces novel taxonomies for classifying techniques, environments, and metrics, reflecting on main problems, and identifying challenges and future directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imitation learning lets an agent learn by copying how someone else does a task. This helps balance how much time it takes to learn and how much effort is needed to get teacher samples. The field has grown quickly, with many new methods and uses. However, there’s no standard way to measure or evaluate results. To fix this, the paper looks at what imitation learning techniques are out there, what environments they work in, and how results are measured. It also talks about common problems and where researchers should go next. |
