PINSKY is a system for open-ended learning through neuroevolution in game-based domains. It builds on the Paired Open-Ended Trailblazer (POET) system, which originally explored learning and environment generation for bipedal walkers, and adapts it to games in the General Video Game AI (GVGAI) system. Previous work showed that by co-evolving levels and neural network policies, levels could be found for which successful policies could not be created via optimization alone. Studied in the realm of Artificial Life as a potentially open-ended alternative to gradient-based fitness, minimal criteria (MC)-based selection helps foster diversity in evolutionary populations. The main question addressed by this paper is how the open-ended learning actually works, focusing in particular on the role of transfer of policies from one evolutionary branch ("species") to another. We analyze the dynamics of the system through creating phylogenetic trees, analyzing evolutionary trajectories of policies, and temporally breaking down transfers according to species type. Furthermore, we analyze the impact of the minimal criterion on generated level diversity and inter-species transfer. The most insightful finding is that inter-species transfer, while rare, is crucial to the system's success.
翻译:PINSKY是一个在以游戏为基础的领域通过神经进化进行开放式学习的系统,它以Paired Opend-Trailblazer(POET)系统为基础,最初探索双足行尸的学习和环境生成,并把它适应通用视频游戏 AI (GVGAI) 系统中的游戏。以前的工作表明,通过共同演化水平和神经网络政策,可以找到单靠优化无法创造成功政策的层次。在人工生活领域研究,作为基于梯度的健身、最低标准(MC)选择的潜在开放替代方法,帮助培养进化人口的多样性。本文讨论的主要问题是开放式学习如何实际发挥作用,特别侧重于政策从一个进化分支(“物种”)转移到另一个分支的作用。我们通过创造植物遗传树、分析政策的进化轨迹和根据物种类型暂时分解转移,分析系统动态。此外,我们分析了最低标准对生成层次多样性和基于MC(MC)选择的多样化和跨物种转移的影响,而最关键的是研究系统之间的转移。