Compares reward shaping, observation augmentation, and loss-sensitivity tuning as post-hoc fairness fixes for Aurora RL congestion control, finding modest reward shaping best preserves throughput while improving fairness in multi-flow settings.
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Fair-Aurora: Comparing Fairness Strategies for Reinforcement Learning-Based Congestion Control in Multi-Flow Environments
Compares reward shaping, observation augmentation, and loss-sensitivity tuning as post-hoc fairness fixes for Aurora RL congestion control, finding modest reward shaping best preserves throughput while improving fairness in multi-flow settings.