Scalable Multi-Objective Reinforcement Learning with Fairness Guarantees using Lorenz Dominance
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910074248626176 |
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| author | Michailidis, Dimitris Röpke, Willem Roijers, Diederik M. Ghebreab, Sennay Santos, Fernando P. |
| author_facet | Michailidis, Dimitris Röpke, Willem Roijers, Diederik M. Ghebreab, Sennay Santos, Fernando P. |
| contents | Multi-Objective Reinforcement Learning (MORL) aims to learn a set of policies that optimize trade-offs between multiple, often conflicting objectives. MORL is computationally more complex than single-objective RL, particularly as the number of objectives increases. Additionally, when objectives involve the preferences of agents or groups, incorporating fairness becomes both important and socially desirable. This paper introduces a principled algorithm that incorporates fairness into MORL while improving scalability to many-objective problems. We propose using Lorenz dominance to identify policies with equitable reward distributions and introduce lambda-Lorenz dominance to enable flexible fairness preferences. We release a new, large-scale real-world transport planning environment and demonstrate that our method encourages the discovery of fair policies, showing improved scalability in two large cities (Xi'an and Amsterdam). Our methods outperform common multi-objective approaches, particularly in high-dimensional objective spaces. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_18195 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scalable Multi-Objective Reinforcement Learning with Fairness Guarantees using Lorenz Dominance Michailidis, Dimitris Röpke, Willem Roijers, Diederik M. Ghebreab, Sennay Santos, Fernando P. Machine Learning Multi-Objective Reinforcement Learning (MORL) aims to learn a set of policies that optimize trade-offs between multiple, often conflicting objectives. MORL is computationally more complex than single-objective RL, particularly as the number of objectives increases. Additionally, when objectives involve the preferences of agents or groups, incorporating fairness becomes both important and socially desirable. This paper introduces a principled algorithm that incorporates fairness into MORL while improving scalability to many-objective problems. We propose using Lorenz dominance to identify policies with equitable reward distributions and introduce lambda-Lorenz dominance to enable flexible fairness preferences. We release a new, large-scale real-world transport planning environment and demonstrate that our method encourages the discovery of fair policies, showing improved scalability in two large cities (Xi'an and Amsterdam). Our methods outperform common multi-objective approaches, particularly in high-dimensional objective spaces. |
| title | Scalable Multi-Objective Reinforcement Learning with Fairness Guarantees using Lorenz Dominance |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2411.18195 |