  There is a deep connection between thermodynamics, information and work
extraction. Ever since the birth of thermodynamics, various types of Maxwell
demons have been introduced in order to deepen our understanding of the second
law. Thanks to them it has been shown that there is a deep connection between
thermodynamics and information, and between information and work in a thermal
system. In this paper, we study the problem of energy extraction from a
thermodynamic system satisfying detailed balance, from an agent with perfect
information, e.g. that has an optimal strategy, given by the solution of the
Bellman equation, in the context of Ising models. We call these agents kobolds,
in contrast to Maxwell's demons which do not necessarily need to satisfy
detailed balance. This is in stark contrast with typical Monte Carlo
algorithms, which choose an action at random at each time step. It is thus
natural to compare the behavior of these kobolds to a Metropolis algorithm. For
various Ising models, we study numerically and analytically the properties of
the optimal strategies, showing that there is a transition in the behavior of
the kobold as a function of the parameter characterizing its strategy.
