基于多个并行CMAC神经网络的强化学习方法

为解决标准Q学习算法收敛速度慢的问题,提出一种基于多个并行小脑模型(Cerebellar Model Articulation Controller:CMAC)神经网络的强化学习方法。该方法通过对输入状态变量进行分割,在不改变状态分辨率的前提下,降低每个状态变量的量化级数,有效减少CMAC的存储空间,将之与Q学习方法相结合,其输出用于逼近状态变量的Q值,从而提高了Q学习方法的学习速度和控制精度,并实现了连续状态的泛化。将该方法用于直线倒立摆的平衡控制中,仿真结果表明了其正确性和有效性。

Reinforcement Learning Based on Many Parallel CMAC Neural Networks

To solve the problem of the slow convergent rate of standard Q-learning, a reinforcement learning algorithm based on many parallel Cerebellar Model Articulation Controller (CMAC) neural networks was proposed. The input state variables were divided to decrease the grades of quantization without changing the resolution. Therefore, the storage spaces of CMAC were reduced effectively, and the outputs of CMAC with lower storage spaces were used to approximate the Q-functions of the corresponding input state variables by integrating CMAC with Q-learning method. So, the learning rate and control precision of Q-algorithm were improved simultaneity, and the generalization of continuous state variables was realized. The method was applied to control the balance of inverted pendulum, and the simulation results show its correctness and efficiency.