時変パラメータを持つ進化的強化学習システム

抄録

In this paper, an evolutionary reinforcement learning system with time-varying parameters that can learn appropriate policy in dynamical POMDPs is proposed. The proposed system has time-varying parameters that can be adjusted by using reinforcement learning. Hence, the system can adapt to the time variation of the dynamical environment even if its variation cannot be observed. In addition, the state space of the environment is divided evolutionarily. So, one need not to divide the state space in advance. The efficacy of the proposed system is shown by mobile robot control simulation under the environment belongs to dynamical POMDPs. The environment is the passage that has gates iterate opening and closing.