Deep Reinforcement Learning for Sequential Combinatorial Auctions
Deep Reinforcement Learning for Sequential Combinatorial Auctions
Revenue-optimal auction design is a challenging problem with significant theoretical and practical implications. Sequential auction mechanisms, known for their simplicity and strong strategyproofness guarantees, are often limited by theoretical results that are largely existential, except for certain restrictive settings. Although traditional reinforcement learning methods such as Proximal Policy Optimization (PPO) …