A new arXiv preprint addresses model misspecification in multi-agent reinforcement learning, a setting where agents' transition dynamics are uncertain. The authors note that such uncertainty is especially problematic because strategic interactions among agents can amplify its effects, making standard approaches fragile.
The paper introduces distributionally robust Markov games (DRMGs) as a framework for this problem. The abstract positions DRMGs as a way to account for transition uncertainty within a game-theoretic setting, though full technical details are not included in the announcement.
Because the source is only an abstract, the article does not describe specific algorithms or results. It is clear, however, that the work targets a known gap: making multi-agent learning robust when the environment model is imperfect.