  In this paper, we treat linear quadratic team decision problems, where a team
of agents minimizes a convex quadratic cost function over $T$ time steps
subject to possibly distinct linear measurements of the state of nature. We
assume that the state of nature is a Gaussian random variable and that the
agents do not know the cost function nor the linear functions mapping the state
of nature to their measurements. We present a gradient-descent based algorithm
with an expected regret of $O(\log(T))$ for full information gradient feedback
and $O(\sqrt(T))$ for bandit feedback. In the case of bandit feedback, the
expected regret has an additional multiplicative term $O(d)$ where $d$ reflects
the number of learned parameters.
