  Recent work has pioneered the use of system-theoretic passivity to study
equilibrium stability for the dynamics of noncooperative strategic interactions
in large populations of learning agents. In this and related works, the
stability analysis leverages knowledge that certain ``canonical'' classes of
learning rules used to model the agents' strategic behaviors satisfy a
passivity condition known as $\delta$-passivity. In this paper, we consider
that agents exhibit learning behaviors that do not align with a canonical
class. Specifically, we focus on characterizing $\delta$-passivity for hybrid
learning rules that combine elements from canonical classes. Our analysis also
introduces and uses a more general version of $\delta$-passivity, which, for
the first time, can handle discontinuous learning rules, including those
showing best-response behaviors. We state and prove theorems establishing
$\delta$-passivity for two broad convex cones of hybrid learning rules. These
cones can merge into a larger one preserving $\delta$-passivity in scenarios
limited to two strategies. In our proofs, we establish intermediate facts that
are significant on their own and could potentially be used to further
generalize our work. We illustrate the applicability of our results through
numerical examples.
