  We model a radar network as an adversarial bandit problem, where the
environment pre-selects reward sequences for each of several actions available
to the network. This excludes environments which vary rewards in response to
the learner's actions. Adversarial environments include those with third party
emitters which enter and exit the environment according to some criteria which
does not depend on the radar network. The network consists of several
independent radar nodes, which attempt to attain the highest possible SINR in
each of many time steps. We show that in such an environment, simple sub-band
selection algorithms are unable to consistently attain high SINR. However,
through the use of adversarial multi-player bandit algorithms, a radar network
can continue to track targets without a loss in tracking precision.
