In this paper, we consider the problem of link adaptation (rate allocation) of Orthogonal Multiple Access Multiple Relay Channel (OMAMRC) using the Multi-Armed Bandit (MAB) online learning framework. The cooperative system is composed of a transmission phase where sources transmit in a round robin manner, and a retransmission phase where a scheduled node sends redundancies. We assume that we have no knowledge of the Channel State Information (CSI) nor of the Channel Distributed Information (CDI). Accordingly, rate allocation must be learned online following a sequential learning algorithm. We adapt to one variant of the MAB framework algorithms, the Upper Confidence Bound (UCB) family, and specifically the UCB1 algorithm. The UCB1 algorithm achieves a logarithmic regret uniformly over time, without any preliminary knowledge about the reward distributions. Due to the exponential growth of the number of arms, following the multiple sources included in the rate allocation, the UCB1 algorithm features a complexity problem. Thus, we propose a sequential UCB1 (SUCB1) algorithm which solves the complexity issue, and outperforms the UCB1 algorithm.