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Calibrating Artificial Guilt: Neurally Grounded Reward Shaping for Prosocial Multi-Agent Reinforcement Learning

arXiv cs.AI ↗ · 2026-08-06 缓存

The paper proposes calibrating an artificial guilt reward signal from human neural and behavioral fMRI data, then embeds it in multi-agent PPO agents. The neurally calibrated agents match human social decision rates far better than hand-tuned or selfish baselines.

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