Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Regret Minimization Approach to Iterative Learning Control   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.

34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
29
The categories of this paper are: funct-an
17
The categories of this paper are: q-fin
28
The categories of this paper are: alg-geom
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg