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While the history of machine learning so far largely encompasses a series ofproblems posed by researchers and algorithms that learn their solutions, animportant question is whether the problems themselves can be generated by thealgorithm at the same time as they are being solved. Such a process would ineffect build its own diverse and expanding curricula, and the solutions toproblems at various stages would become stepping stones towards solving evenmore challenging problems later in the process. The Paired Open-EndedTrailblazer (POET) algorithm introduced in this paper does just that: it pairsthe generation of environmental challenges and the optimization of agents tosolve those challenges. It simultaneously explores many different paths throughthe space of possible problems and solutions and, critically, allows thesestepping-stone solutions to transfer between problems if better, catalyzinginnovation. The term open-ended signifies the intriguing potential foralgorithms like POET to continue to create novel and increasingly complexcapabilities without bound. Our results show that POET produces a diverse rangeof sophisticated behaviors that solve a wide range of environmental challenges,many of which cannot be solved by direct optimization alone, or even through adirect-path curriculum-building control algorithm introduced to highlight thecritical role of open-endedness in solving ambitious challenges. The ability totransfer solutions from one environment to another proves essential tounlocking the full potential of the system as a whole, demonstrating theunpredictable nature of fortuitous stepping stones. We hope that POET willinspire a new push towards open-ended discovery across many domains, wherealgorithms like POET can blaze a trail through their interesting possiblemanifestations and solutions.

Paired Open-Ended Trailblazer (POET): Endlessly Generating Increasingly Complex and Diverse Learning Environments and Their Solutions