Summary of Fairness in Reinforcement Learning: a Survey, by Anka Reuel et al.
Fairness in Reinforcement Learning: A Survey
by Anka Reuel, Devin Ma
First submitted to arxiv on: 11 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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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 surveys the current state of fairness in reinforcement learning (RL), focusing on ensuring responsible development and deployment of RL-enabled systems like autonomous vehicles. It reviews where fairness considerations can arise, discusses definitions of fairness, and highlights methodologies used to implement fairness in single- and multi-agent systems. The authors also showcase application domains that have investigated fair RL and identify gaps in the literature, such as understanding fairness in RLHF, which need to be addressed for operationalizing fair RL in real-world systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fairness in machine learning is important, but we don’t know much about it when it comes to reinforcement learning. This type of learning is used in self-driving cars and other complex systems that make decisions over time. The researchers looked at where fairness comes into play in these systems, what fairness means, and how people have tried to make things fair. They also showed examples of how fairness has been explored in different areas, like robots working together or cars making decisions. But they found some gaps in the research that need to be fixed before we can use fair reinforcement learning in real-world situations. |
Keywords
» Artificial intelligence » Machine learning » Reinforcement learning » Rlhf
