Mingwei Ma

I am a Ph.D. student at the University of Chicago, Booth School of Business, where I specialize in statistics and economics.

Prior to that, I obtained my MSc in Mathematical and Theoretical Physics and BA in Physics and Philosophy, both from the University of Oxford. I was awarded First Class Honors (1:1) and the Gibbs Prize for Best Performance.

My research interest is in financial asset pricing, statistical machine learning, and multi-agent reinforcement learning. Here is the link to my academic CV (Last updated: 2020/02).

Time present and time past
Are both perhaps present in time future,
And time future contained in time past.

               T. S. Eliot, Burnt Norton

      Abstract: An unaddressed challenge in zero-shot coordination is to take advantage of the semantic relationship between the features of an action and the features of observations. Humans take advantage of these relationships in highly intuitive ways. For instance in the absence of a shared-language, we might point to the object we desire or hold up fingers to indicate how many objects we want. To address this challenge, we investigate the effect of network architecture on the propensity of learning algorithms to make use of these relationships in human-compatible ways. We find that attention-based architectures that jointly process a featurized representation of the observation and the action, have a better inductive bias for exploiting semantic relationships for zero-shot coordination. Excitingly, in a set of diagnostic tasks, these agents produce highly human-compatible policies, without requiring the symmetry relationships of the problems to be hard-coded.

Mingwei Ma*, Jizhou Liu*, Sam Sokota, Max Kleiman-Weiner, Jakob Foerester (2021). Zero-Shot Coordination via Semantic Relationships Between Actions and Observations, submitted.