基于RBFNN的强化学习在机器人导航中的应用

在复杂连续环境下，强化学习系统的状态空间面临维数灾难问题，需要采取量化的方法，降低输入空间的复杂度。径向基神经网络(RBFNN:Radial Basis Function Neural Networks)具有较强的函数逼近能力及泛化能力，由此提出了基于径向基神经网络的Q学习方法,并将其应用于单机器人的自主导航。在基于径向基神经网络的强化学习系统中，用径向基神经网络逼近状态空间和Q函数，使学习系统具有良好的泛化能力。仿真结果表明,该导航方法具有较强的避碰能力，提高了机器人对环境的适应能力。

Application of Reinforcement Learning Based on Radial Basis Function Neural Networks in Robot Navigation

In a complex and continuous environment,Reinforcement Learning system will cause the dimensional disaster and generalization is often adopted to reduce the complexity of input space.Radial Basis Function Neural Networks(RBFNN:Radial Basis Function Neural Networks) has the function of strong approximation and generalization.Reinforcement Learning based on RBFNN is proposed,and it is used in the single-robot navigation.In the learning system,the state space and Q function are approximated by RBFNN.Simulation ...