  We propose \emph{Choquet regularizers} to measure and manage the level of
exploration for reinforcement learning (RL), and reformulate the
continuous-time entropy-regularized RL problem of Wang et al. (2020, JMLR,
21(198)) in which we replace the differential entropy used for regularization
with a Choquet regularizer. We derive the Hamilton--Jacobi--Bellman equation of
the problem, and solve it explicitly in the linear--quadratic (LQ) case via
maximizing statically a mean--variance constrained Choquet regularizer. Under
the LQ setting, we derive explicit optimal distributions for several specific
Choquet regularizers, and conversely identify the Choquet regularizers that
generate a number of broadly used exploratory samplers such as
$\epsilon$-greedy, exponential, uniform and Gaussian.
