GACEM: Generalized Autoregressive Cross Entropy Method for Multi-Modal Black Box Constraint Satisfaction
This addresses multi-modal black-box constraint satisfaction for optimization tasks, but it is incremental as it builds on existing CEM methods.
The paper tackles black-box optimization and constraint satisfaction by developing a modified Cross-Entropy Method (CEM) that uses a masked auto-regressive neural network to model uniform distributions, enabling multi-modal solution tracking. It demonstrates better performance in diverse solutions, mode discovery, and sample efficiency compared to CEM variations.
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.