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Expert problem-solving is driven by powerful languages for thinking aboutproblems and their solutions. Acquiring expertise means learning theselanguages -- systems of concepts, alongside the skills to use them. We presentDreamCoder, a system that learns to solve problems by writing programs. Itbuilds expertise by creating programming languages for expressing domainconcepts, together with neural networks to guide the search for programs withinthese languages. A ``wake-sleep'' learning algorithm alternately extends thelanguage with new symbolic abstractions and trains the neural network onimagined and replayed problems. DreamCoder solves both classic inductiveprogramming tasks and creative tasks such as drawing pictures and buildingscenes. It rediscovers the basics of modern functional programming, vectoralgebra and classical physics, including Newton's and Coulomb's laws. Conceptsare built compositionally from those learned earlier, yielding multi-layeredsymbolic representations that are interpretable and transferrable to new tasks,while still growing scalably and flexibly with experience.

DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning