Scalable Multi-Objective Reinforcement Learning with Fairness Guarantees using Lorenz Dominance

• URL: http://arxiv.org/abs/2411.18195v1
• Date: Wed, 27 Nov 2024 10:16:25 GMT
• Title: Scalable Multi-Objective Reinforcement Learning with Fairness Guarantees using Lorenz Dominance
• Authors: Dimitris Michailidis, Willem Röpke, Diederik M. Roijers, Sennay Ghebreab, Fernando P. Santos,
• Abstract summary: Multi-Objective Reinforcement Learning (MORL) aims to learn a set of policies that optimize trade-offs between multiple, often conflicting objectives. This paper introduces a principled algorithm that incorporates fairness into MORL while improving scalability to many-objective problems.
• Score: 43.44913206006581
• License:
• Abstract: 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, ensuring fairness is 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.

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