Advanced materials are needed to further next-generation technologies such as
quantum computing, carbon capture, and low-cost medical imaging. However,
advanced materials discovery is confounded by two fundamental challenges: the
challenge of a high-dimensional, complex materials search space and the
challenge of combining knowledge, i.e., data fusion across instruments and
labs. To overcome the first challenge, researchers employ knowledge of the
underlying material synthesis-structure-property relationship, as a material's
structure is often predictive of its functional property and vice versa. For
example, optimal materials often occur along composition-phase boundaries or
within specific phase regions. Additionally, knowledge of the
synthesis-structure-property relationship is fundamental to understanding
underlying physical mechanisms. However, quantifying the
synthesis-structure-property relationship requires overcoming the second
challenge. Researchers must merge knowledge gathered across instruments,
measurement modalities, and even laboratories. We present the
Synthesis-structure-property relAtionship coreGionalized lEarner (SAGE)
algorithm. A fully Bayesian algorithm that uses multimodal coregionalization to
merge knowledge across data sources to learn synthesis-structure-property
relationships.

应用贝叶斯算法的 SAGE 算法通过多模态核主成分分析将来自不同数据源的知识合并，学习合成 - 结构 - 性能关系，从而克服高维复杂材料搜索空间和知识融合的挑战。

通过数据融合学习材料的结构 - 性能关系：贝叶斯共区相关 N 维分段函数学习

Learning material synthesis-structure-property relationship by data  fusion: Bayesian Co-regionalization N-Dimensional Piecewise Function Learning

Model-based reinforcement learning algorithms are typically more sample
efficient than their model-free counterparts, especially in sparse reward
problems. Unfortunately, many interesting domains are too complex to specify
the complete models required by traditional model-based approaches. Learning a
model takes a large number of environment samples, and may not capture critical
information if the environment is hard to explore. If we could specify an
incomplete model and allow the agent to learn how best to use it, we could take
advantage of our partial understanding of many domains. Existing hybrid
planning and learning systems which address this problem often impose highly
restrictive assumptions on the sorts of models which can be used, limiting
their applicability to a wide range of domains. In this work we propose SAGE,
an algorithm combining learning and planning to exploit a previously unusable
class of incomplete models. This combines the strengths of symbolic planning
and neural learning approaches in a novel way that outperforms competing
methods on variations of taxi world and Minecraft.

本文提出新的算法 SAGE，结合符号性规划与神经网络学习等方法，以克服传统模型的局限，更高效地解决基于模型的强化学习在处理部分了解环境时遇到的问题。该算法在出租车环境和 Minecraft 等变化场景中的表现优于其他方法。