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DNQ: Deep Nash Q-Network for Partially Observable n-Player Games

paperactivednq-deep-nash-q-network-for-partially-observable-n-player-games-afad764a·1 events·first seen Jun 5, 2026

Aliases: DNQ: Deep Nash Q-Network for Partially Observable n-Player Games

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4arXiv · cs.LG·Jun 5, 2026·source ↗

DNQ: Deep Nash Q-Network framework for equilibrium learning in multi-agent bidding games

Researchers propose DNQ (Deep Nash Q-Network), a solver-in-the-loop framework for training agents to reach Nash equilibria in partially observable n-player simultaneous bidding games. The method alternates between trajectory collection, critic-based payoff estimation, external equilibrium computation, and policy imitation via KL divergence minimization. A scalable pairwise payoff formulation is shown to outperform the exact N-player tensor approach in computational cost while maintaining strategic quality, with experiments demonstrating the trade-off between fidelity and scalability as agent count grows.

DNQ DNQ: Deep Nash Q-Network for Partially Observable n-Player Games