Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning. (arXiv:2205.02450v1 [cs.LG])

Web: http://arxiv.org/abs/2205.02450

Dynamic mechanism design has garnered significant attention from both
computer scientists and economists in recent years. By allowing agents to
interact with the seller over multiple rounds, where agents' reward functions
may change with time and are state dependent, the framework is able to model a
rich class of real world problems. In these works, the interaction between
agents and sellers are often assumed to follow a Markov Decision Process (MDP).
We focus on the setting where the reward and …

arxiv design learning reinforcement reinforcement learning