Summary of Fair Resource Allocation in Weakly Coupled Markov Decision Processes, by Xiaohui Tu et al.
Fair Resource Allocation in Weakly Coupled Markov Decision Processes
by Xiaohui Tu, Yossiri Adulyasak, Nima Akbarzadeh, Erick Delage
First submitted to arxiv on: 14 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 a framework for fair resource allocation in sequential decision-making environments, which are modeled as weakly coupled Markov decision processes. The authors introduce a new fairness definition based on the generalized Gini function, and show that the problem reduces to optimizing the utilitarian objective over permutation-invariant policies in the homogeneous case. The paper also proposes a count-proportion-based deep reinforcement learning approach for the general setting, which is validated through comprehensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to fairly share resources among different decision-making agents when they make choices one after another. It uses a special kind of mathematical model called a Markov decision process and introduces a new way to measure fairness. The authors show that in some cases, the problem becomes easier to solve if all the agents are treated equally. They also develop a new method for solving this problem using machine learning algorithms. Finally, they test their ideas with simulations and find that they work well. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
