AI Seminar Series 2023: Zichen Zhang

The AI Seminar is a weekly meeting at the University of Alberta where researchers interested in artificial intelligence (AI) can share their research. Presenters include both local speakers from the University of Alberta and visitors from other institutions. Topics can be related in any way to artificial intelligence, from foundational theoretical work to innovative applications of AI techniques to new fields and problems.

On May 19, Zichen Zhang —Phd student at the University of Alberta — presented “A Simple Decentralized Cross-Entropy Method" at the AI Seminar.

In this talk, Zhang presents a simple extension to the Cross-Entropy Method (CEM), a gradient-free optimization method frequently used for planning in model-based reinforcement learning (MBRL).

The classical CEM employs a centralized approach to update the sampling distribution based on a global top-k operation’s results on samples. However, he demonstrates that this approach can make CEM prone to local optima, thus impairing its sample efficiency. To address this issue, Zhang proposes Decentralized CEM (DecentCEM), a simple yet effective improvement over classical CEM, by using an ensemble of CEM instances running independently from one another, and each performing a local improvement of its own sampling distribution. He shows in an optimization task that DecentCEM finds the global optimum more consistently than CEM that uses either a single or even a mixture of Gaussian distributions. Notably, this improvement does not compromise CEM’s convergence guarantee. When applied to MBRL planning problems in continuous control environments, DecentCEM shows an improved sample efficiency, with only a reasonable increase in computational cost.

For those interested in exploring this work further, please check out the paper at:

And the code is available at

Watch the full presentation below:

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