Strategyproof Deep Reinforcement Learning for Online Resource Allocation

The paper introduces the first strategyproof deep reinforcement learning mechanism for online resource allocation, combining a monotonic neural network, a generalized critical payment rule, and training via RL or imitation. This design ensures truthful reporting and individual rationality while capturing nonlinear resource interactions beyond linear models. Experiments on a four‑resource stream show the neural rule attains 97.8% of an offline welfare upper bound, outperforming linear learners and first‑come, first‑served baselines, and imitation training achieves comparable performance 89 times faster than RL.

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• agents
• reinforcement-learning