Reinforcement learning (RL) has been proven effective in many scenarios, including environment exploration and motion planning. However, its application in data-driven character control has produced relatively simple motion results compared to recent approaches that have used large complex motion data without RL. In this paper, we provide a real-time motion control method that can generate high-quality and complex motion results from various sets of unstructured data while retaining the advantage of using RL, which is the discovery of optimal behaviors by trial and error. We demonstrate the results for a character achieving different tasks, from simple direction control to complex avoidance of moving obstacles. Our system works equally well on biped/quadruped characters, with motion data ranging from 1 to 48 minutes, without any manual intervention. To achieve this, we exploit a finite set of discrete actions, where each action represents full-body future motion features. We first define a subset of actions that can be selected in each state and store these pieces of information in databases during the preprocessing step. The use of this subset of actions enables the effective learning of control policy even from a large set of motion data. To achieve interactive performance at run-time, we adopt a proposal network and a k-nearest neighbor action sampler.