Summary of Social Learning Through Interactions with Other Agents: a Survey, by Dylan Hillier et al.
Social Learning through Interactions with Other Agents: A Survey
by Dylan Hillier, Cheston Tan, Jing Jiang
First submitted to arxiv on: 31 Jul 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 paper explores the concept of social learning in machine learning, examining how embodied agents can utilize social learning techniques. It highlights the parallels between human social learning and machine learning, including behavioral cloning, next-token prediction, and learning from human feedback. The study finds that individual social learning techniques have been used successfully, but there is a need for unifying work to bring these techniques together to create socially embodied agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can learn like humans do. It compares how people learn from each other with how machines learn. The researchers found some ways that machines can imitate human behavior and learn from feedback, but they’re still working on creating truly social machines that can communicate and learn together. |
Keywords
» Artificial intelligence » Machine learning » Token
