  Meta-learning from learning curves is an important yet often neglected
research area in the Machine Learning community. We introduce a series of
Reinforcement Learning-based meta-learning challenges, in which an agent
searches for the best suited algorithm for a given dataset, based on feedback
of learning curves from the environment. The first round attracted participants
both from academia and industry. This paper analyzes the results of the first
round (accepted to the competition program of WCCI 2022), to draw insights into
what makes a meta-learner successful at learning from learning curves. With the
lessons learned from the first round and the feedback from the participants, we
have designed the second round of our challenge with a new protocol and a new
meta-dataset. The second round of our challenge is accepted at the AutoML-Conf
2022 and currently ongoing .
