Zero-Shot Coordination via Semantic Relationships Between Actions and Observations
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.
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