论文标题
GACEM：多模式黑匣子约束满意度的广义自回旋横熵方法
GACEM: Generalized Autoregressive Cross Entropy Method for Multi-Modal Black Box Constraint Satisfaction
论文作者
论文摘要
在这项工作中，我们提出了一种新的黑盒优化和约束满意度的方法。试图解决此问题的现有算法无法考虑多种模式，并且无法适应环境动态的变化。为了解决这些问题，我们开发了一种修改的跨凝结方法（CEM），该方法使用掩盖的自动回归神经网络在解决方案空间上对均匀分布进行建模。我们使用增强学习中的最大熵政策梯度方法训练该模型。我们的算法能够表达复杂的解决方案空间，从而使其能够跟踪各种不同的解决方案区域。我们从经验上将我们的算法与CEM的变化进行比较，其中包括具有固定差异的高斯之前的算法，并在以下方面表现出更好的性能：多种解决方案的数量，在多模式问题中发现更好的模式发现以及在某些情况下的样本效率更好。
In this work we present a new method of black-box optimization and constraint satisfaction. Existing algorithms that have attempted to solve this problem are unable to consider multiple modes, and are not able to adapt to changes in environment dynamics. To address these issues, we developed a modified Cross-Entropy Method (CEM) that uses a masked auto-regressive neural network for modeling uniform distributions over the solution space. We train the model using maximum entropy policy gradient methods from Reinforcement Learning. Our algorithm is able to express complicated solution spaces, thus allowing it to track a variety of different solution regions. We empirically compare our algorithm with variations of CEM, including one with a Gaussian prior with fixed variance, and demonstrate better performance in terms of: number of diverse solutions, better mode discovery in multi-modal problems, and better sample efficiency in certain cases.
