Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach

arXiv:2202.12797v2 Announce Type: replace
Abstract: Dynamic mechanism design studies how mechanism designers should allocate resources among agents in a time-varying environment. We consider the problem where the agents interact with the mechanism designer according to an unknown Markov Decision Process (MDP), where agent rewards and the mechanism designer's state evolve according to an episodic MDP with unknown reward functions and transition kernels. We focus on the online setting with linear function approximation and propose novel learning algorithms to recover the …

abstract agent agents arxiv cs.gt cs.lg decision design designer designers dynamic environment environments markov math.oc process reinforcement reinforcement learning resources state stat.ml studies type