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MA-UITD: Offline Multi-Agent Reward Inference for Characterizing Continuous Social Behavior
Multi-agent inverse reinforcement learning offers a powerful framework not only for imitation learning but also for understanding the neural computations supporting social interactions and decision-making. Inferring rewards from interacting agents is challenging because observed choices reflect both
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