Open-ended learning (OEL) is receiving rapidly growing attention in recent years, as deep learning models become ever more adept at learning meaningful and useful behaviors from web-scale data. Improving the performance and generality of such models depends greatly on our ability to continue to collect new and useful training data. OEL systems co-evolve the learning agent (e.g. the model) with its environment or other sources of training data, resulting in the continued, active generation of new training data specifically useful for the current agent or model. Conceivably such OEL processes, if designed appropriately, can lead to models exhibiting increasingly general capabilities. However, it remains an open problem to produce a truly open-ended system in practice, one that endlessly generates meaningfully novel data. We hope our workshop provides a forum both for bridging knowledge across a diverse set of relevant fields as well as sparking new insights that can enable truly open-ended learning systems.