FairTest: Search-Based Fairness Testing for Multi-Agent Reinforcement Learning Systems
Multi-agent Reinforcement Learning (MARL) trains a team of agents that share one environment and learn their policies together. Training maximizes the team return, and a high return does not imply that the rewards are shared fairly among the agents in every episode. Testing is an established way to discover the failures of deep reinforcement learning, yet few methods address the fairness of MARL. In this work, we propose FairTest, a search-based testing approach that seeks the unfair executions of a MARL policy. The design combines search guidance with test prioritization. The guidance scores each candidate with three fitness functions. One measures the fairness of the runs already perfor...
| Authors | Xiaotong Wang, Xuan Xie |
|---|---|
| Primary category | cs.SE |
| Published | 23 September 2026 |
| Last updated at source | 23 September 2026 |
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