突现进化课程中的迁移动力
Transfer Dynamics in Emergent Evolutionary Curricula
摘要：
PINSKY是一个通过神经进化在游戏领域进行开放式学习的系统。它建立在Paired Open-Ended Trailblazer（POET）系统的基础上，该系统最初探索了双足步行者的学习和环境生成，并将其应用于通用视频游戏AI（GVGAI）系统中的游戏。以前的工作表明，通过共同进化的水平和神经网络政策，可以找到水平，成功的政策不能通过单独的优化创建。在人工生命领域研究作为一个潜在的开放式替代梯度为基础的健身，最小标准（MC）为基础的选择有助于促进进化种群的多样性。本文讨论的主要问题是开放式学习实际上是如何工作的，特别关注从一个进化分支（“物种”）到另一个进化分支的政策转移的作用。我们通过创建系统发育树，分析政策的演变轨迹，并根据物种类型在时间上分解转移来分析系统的动态。此外，我们分析了最小准则对生成水平多样性和物种间转移的影响。最具洞察力的发现是，物种间的转移虽然罕见，但对系统的成功至关重要。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
