POET: Open-Ended Coevolution of Environments and their Optimized Solutions

How can progress in machine learning and reinforcement learning be automated to generate its own never-ending curriculum of challenges without human intervention? The recent emergence of quality diversity (QD) algorithms offers a glimpse of the potential for such continual open-ended invention. For example, novelty search showcases the benefits of explicit novelty pressure, MAP-Elites and Innovation Engines highlight the advantage of explicit elitism within niches in an otherwise divergent process, and minimal criterion coevolution (MCC) reveals that problems and solutions can coevolve divergently. The Paired Open-Ended Trailblazer (POET) algorithm introduced in this paper combines these principles to produce a practical approach to generating an endless progression of diverse and increasingly challenging environments while at the same time explicitly optimizing their solutions. An intriguing implication is the opportunity to transfer solutions among environments, reflecting the view that innovation is a circuitous and unpredictable process. POET is tested in a 2-D obstacles course domain, where it generates diverse and sophisticated behaviors that create and solve a wide range of environmental challenges, many of which cannot be solved by direct optimization, or by a direct-path curriculum-building control algorithm. We hope that POET will inspire a new push towards open-ended discovery across many domains.