Strategic Decision-Making in the Presence of Information Asymmetry: Provably Efficient RL with Algorithmic Instruments

Understand

We study offline reinforcement learning under a novel model called strategic MDP, which characterizes the strategic interactions between a principal and a sequence of myopic agents with private types.

• Due to the bilevel structure and private types, strategic MDP involves information asymmetry between the principal and the agents.
• We focus on the offline RL problem, where the goal is to learn the optimal policy of the principal concerning a target population of agents based on a pre-collected dataset that consists of historical interactions.
• The unobserved private types confound such a dataset as they affect both the rewards and observations received by the principal.