Most restless Markovian bandits problems in infinite horizon can be solved quasioptimally: the famous Whittle index policy is known to become asymptotically optimal exponentially fast as the number of arms grows, at least under certain conditions (including having a so-called indexable problem). For restless Markovian bandits problems in finite horizons no such optimal policy is known. In this paper, we define a new policy, based on a linear program relaxation of the finite horizon problem (called the LP-filling policy), that is asymptotically optimal under no condition. Furthermore we show that for regular problems (defined in the paper) the LP-filling policy becomes an index policy (called the LP-regular policy) and becomes optimal exponentially fast in the number of arms. We also introduce the LP-update policy that significantly improves the performance compared to the LP-filling policy for large time horizons. We provide numerical studies that show the prevalence of our LP-policies over previous solutions.