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Meta-learning from learning curves is an important yet often neglectedresearch area in the Machine Learning community. We introduce a series ofReinforcement Learning-based meta-learning challenges, in which an agentsearches for the best suited algorithm for a given dataset, based on feedbackof learning curves from the environment. The first round attracted participantsboth from academia and industry. This paper analyzes the results of the firstround (accepted to the competition program of WCCI 2022), to draw insights intowhat makes a meta-learner successful at learning from learning curves. With thelessons learned from the first round and the feedback from the participants, wehave designed the second round of our challenge with a new protocol and a newmeta-dataset. The second round of our challenge is accepted at the AutoML-Conf2022 and currently ongoing .

Meta-learning from Learning Curves Challenge: Lessons learned from the First Round and Design of the Second Round