Advances in Computer Science : an International Journal (ACSIJ)
Motion and locomotion planning have a wide area of usage in different fields. Locomotion planning with premade character animations has been highly noticed in recent years. Reinforcement Learning (RL) presents promising ways to create motion planners using premade character animations. Although RL-based motion planners offer great ways to control character animations but they have some problems that make them hard to be used in practice, including high dimensionality and environment dependency. In this paper, the authors present a motion planner which can fulfill its motion tasks by selecting its best animation sequences in different environments without any previous knowledge of the environment.