Learning When to Cooperate Under Heterogeneous Goals
A significant element of human cooperative intelligence lies in our ability to identify opportunities for fruitful collaboration; and conversely to recognise when the task at hand is better pursued alone. Research on flexible cooperation in machines has left this meta-level problem largely unexplored, despite its importance for successful collaboration in heterogeneous open-ended environments. Here, we extend the typical Ad Hoc Teamwork (AHT) setting to incorporate the idea of agents having heterogeneous goals that in any given scenario may or may not overlap. We introduce a novel approach to learning policies in this setting, based on a hierarchical combination of imitation and reinforce...
| Authors | Max Taylor-Davies, Neil Bramley, Christopher G. Lucas |
|---|---|
| Primary category | cs.MA |
| Published | 7 March 2026 |
| Last updated at source | 7 March 2026 |
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