Creatures in the real world constantly encounter new and diverse challenges they have never seen before. They will often need to adapt to some of these tasks and solve them in order to survive. This almost endless world of novel challenges is not as common in virtual environments, where artificially evolving agents often have a limited set of tasks to solve. An exception to this is the field of open-endedness where the goal is to create unbounded exploration of interesting artefacts. We want to move one step closer to creating simulated environments similar to the diverse real world, where agents can both find solvable tasks, and adapt to them. Through the use of MAP-Elites we create a structured repertoire, a map, of terrains and virtual creatures that locomote through them. By using novelty as a dimension in the grid, the map can continuously develop to encourage exploration of new environments. The agents must adapt to the environments found, but can also search for environments within each cell of the grid to find the one that best fits their set of skills. Our approach combines the structure of MAP-Elites, which can allow the virtual creatures to use adjacent cells as stepping stones to solve increasingly difficult environments, with open-ended innovation. This leads to a search that is unbounded, but still has a clear structure. We find that while handcrafted bounded dimensions for the map lead to quicker exploration of a large set of environments, both the bounded and unbounded approach manage to solve a diverse set of terrains.

翻译：真实世界中的生物不断面临前所未有的新奇多样挑战。它们常常需要适应其中某些任务并加以解决以求生存。这种近乎无尽的新奇挑战世界在虚拟环境中并不常见——人工进化智能体通常只需解决有限的任务集。开放式进化领域是个例外，其目标是创造对有趣人工制品无边界探索的系统。我们旨在更进一步构建类似真实世界多样性的模拟环境，使智能体既能发现可解任务，又能适应这些任务。通过运用MAP-Elites算法，我们构建了地形与穿行其中的虚拟生物的结构化库——即"地图"。以新颖性作为网格维度，该地图可持续发展以鼓励对新环境的探索。智能体必须适应所发现的环境，同时也能在每个网格单元内搜索最适合其技能组合的环境。我们的方法将MAP-Elites的结构（允许虚拟生物利用相邻单元作为踏脚石攻克日益复杂的环境）与开放式创新相结合，实现了既有明确框架又不设限的搜索。研究发现：虽然人工设计的边界化维度能快速探索大量环境，但无论采用有界还是无界方法，都能成功解决多样化的地形挑战。