  We consider the setting of iterative learning control, or model-based policy
learning in the presence of uncertain, time-varying dynamics. In this setting,
we propose a new performance metric, planning regret, which replaces the
standard stochastic uncertainty assumptions with worst case regret. Based on
recent advances in non-stochastic control, we design a new iterative algorithm
for minimizing planning regret that is more robust to model mismatch and
uncertainty. We provide theoretical and empirical evidence that the proposed
algorithm outperforms existing methods on several benchmarks.
