The large spectral bandwidth at millimeter-wave (mmWave) frequencies provides a mean to achieve very high data rates in wireless communication systems. A unique characteristic of mmWave is that mmWave links are very sensitive to blockage and have large propagation path loss, which exhibits low line-of-sight (LoS) probability, unstable connectivity and unreliable communication. To overcome such challenges, one of the existing solution is to associate the user equipment (UE) with other available Base Stations (BSs) by handover (HO) if the serving BS is blocked. In this paper, for a pedestrian scenario, we propose two reinforcement learning (RL) based user association algorithms, which accounts for the past experience of the blockage on the position of the UE. One focuses on the reward to increase the sum LoS probability and is named as Blockage-Aware User Association (BAUA). The other focuses on the reward to balance the tradeoff between the throughput and the LoS probability and is named as modified BAUA. Simulation results show that the BAUA algorithm increased sum LoS probability and the modified BAUA algorithm show better trade-off between the throughput and the LoS probability than the maximum Signal-to-Interference-plus-Noise Ratio (SINR) based and maximum-throughput based user association algorithms.