DNQ

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.