The A3M framework addresses the challenges of learning to bid in repeated multi-unit auctions by integrating adaptive deep reinforcement learning, adversarial reasoning, and multi-objective reward design. It utilizes an actor-critic backbone and opponent modeling to optimize strategy against non-stationary adversaries while balancing utility, revenue, and fairness.
- Reduces final regret by 30--40% in standard settings compared to established baselines.
- Maintains robust performance against adversarial strategy shifts through fictitious play.
- Scales favorably with the number of units K and enables tunable multi-objective trade-offs.
- Validated via comprehensive empirical evaluation in both discriminatory and uniform price auctions.
The authors establish A3M as a powerful and flexible framework for learning in complex auction environments, demonstrating that its core components are necessary for effective strategic bidding.