Learning automata (LA), which intellectually explores its optimal state by interacting with an external environment continuously, is encountered widely in artificial intelligence. In the evaluation of LA, it has always been a key issue how to tradeoff between “accuracy” and “speed,” which substantially touches on parameter tuning. A latest issue in the design of LA methodology involves bearing a parameter-free property, thus removing the tremendous expenses brought by parameter tuning. Nevertheless, the currently existing parameter-free LA schemes generally maintain a Monte-Carlo technique, which helps avoid the tuning process at the cost of more computations. This paper examines a new measurement of parameter-free LA schemes based on statistics which overcome the difficulties found in other counterparts. Specifically, it has innovatively disengaged from the dependance on Monte-Carlo methods. Of greater significance, the learning mechanisms operating in the common stationary environments are likewise extended to the nonstationary environments. Simulations confirm the effectiveness and efficiency of the proposed algorithm, especially its low computation consumption as well as the strong tracking capability to abrupt environmental changes.

中文翻译：

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基于两类统计的非蒙特卡罗无参数学习自动机方案


学习自动机（LA）通过不断与外部环境交互来智能地探索其最佳状态，在人工智能中广泛存在。在LA的评估中，如何权衡“精度”和“速度”一直是一个关键问题，这实质上涉及到参数调整。 LA方法设计的最新问题是具有无参数特性，从而消除了参数调整带来的巨大开销。然而，目前现有的无参数 LA 方案通常保持蒙特卡罗技术，这有助于避免以更多计算为代价的调整过程。本文研究了一种基于统计的无参数 LA 方案的新测量方法，克服了其他同行中发现的困难。具体来说，它创新性地摆脱了对蒙特卡洛方法的依赖。更重要的是，在常见的静态环境中运行的学习机制同样可以扩展到非静态环境。仿真验证了该算法的有效性和效率，特别是其低计算消耗以及对环境突变的强大跟踪能力。