DNQ: Deep Nash Q-Network for Partially Observable n-Player Games
Many real-world competitive systems require multiple decision-makers to act simultaneously under shared constraints, limited information, and repeated interaction, as in auctions, resource allocation, and security competition. We study multi-turn simultaneous bidding as a controlled testbed for such problems and propose DNQ, a solver-in-the-loop equilibrium supervision framework for training bidding agents. DNQ alternates between trajectory collection, critic-based payoff estimation, equilibrium computation, and policy imitation. At each visited state, a shared critic predicts either pairwise payoff matrices or an exact N-player payoff tensor, an external solver computes equilibrium strat...
| Authors | Qintong Xie, Edward Koh, Xavier Cadet et al. |
|---|---|
| Primary category | cs.GT |
| Published | 4 June 2026 |
| Last updated at source | 16 July 2026 |
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