Infinite-horizon extensions of reward-free warm-up and MAIL-WARM

Establish whether analogous sample complexity guarantees for interactive Multi-Agent Imitation Learning can be obtained in the infinite-horizon discounted setting, by developing reward-free exploration and analysis tools that do not rely on finite-horizon-specific algorithms such as EULER.

Background

The proposed MAIL-WARM framework and its analysis rely on finite-horizon regret guarantees for EULER used in the reward-free warm-up phase. Extending this approach to infinite horizons is non-trivial because the underlying exploration and regret tools differ significantly.

Resolving this would broaden the applicability of interactive MAIL to discounted infinite-horizon Markov games and may yield advances of independent interest in reward-free reinforcement learning.