  In unsupervised environment design, reinforcement learning agents are trained
on environment configurations (levels) generated by an adversary that maximises
some objective. Regret is a commonly used objective that theoretically results
in a minimax regret (MMR) policy with desirable robustness guarantees; in
particular, the agent's maximum regret is bounded. However, once the agent
reaches this regret bound on all levels, the adversary will only sample levels
where regret cannot be further reduced. Although there are possible performance
improvements to be made outside of these regret-maximising levels, learning
stagnates. In this work, we introduce Bayesian level-perfect MMR (BLP), a
refinement of the minimax regret objective that overcomes this limitation. We
formally show that solving for this objective results in a subset of MMR
policies, and that BLP policies act consistently with a Perfect Bayesian policy
over all levels. We further introduce an algorithm, ReMiDi, that results in a
BLP policy at convergence. We empirically demonstrate that training on levels
from a minimax regret adversary causes learning to prematurely stagnate, but
that ReMiDi continues learning.
