Summary of Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-motive Games, by Fanqi Kong et al.
Learning to Balance Altruism and Self-interest Based on Empathy in Mixed-Motive Games
by Fanqi Kong, Yizhe Huang, Song-Chun Zhu, Siyuan Qi, Xue Feng
First submitted to arxiv on: 10 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 The paper proposes LASE, a distributed multi-agent reinforcement learning algorithm that balances altruism and self-interest in mixed-motive games. LASE allocates rewards as gifts to co-players based on their social relationships, which are estimated using counterfactual reasoning. This approach fosters cooperation while preventing exploitation. The algorithm is tested in spatially and temporally extended mixed-motive games, demonstrating its ability to promote group collaboration without compromising fairness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper talks about artificial intelligence agents that can work together to achieve a common goal. These agents need to balance being helpful (altruistic) with taking care of themselves (self-interested). The authors propose an algorithm called LASE that helps these agents work together without getting taken advantage of. They tested this algorithm in different scenarios and found it works well. |
Keywords
» Artificial intelligence » Reinforcement learning
