中文摘要：

一个新奇方法被设计解决与人工的潜在的地学习问题的加强。第一，学习问题的加强被转移到由使用人工的潜在的地(APF ) 计划问题的一条路径，它是一个很适当的方法为学习问题的加强建模。第二，一个新 APF 算法被建议与一个虚拟水流动概念在潜在的地方法克服本地最小的问题。这个新方法的表演被作为关键说出的一个格子世界问题和门迷宫测试。试验性的结果证明在 45 试用以内，好、确定的政策在几乎所有模拟被发现。与 WIERING 需要 20 的学习 HQ 系统比较为稳定的答案的 000 试用，建议新方法能比学习 HQ 快速更加获得最佳、稳定的政策。因此，新方法简单、有效把一个最佳的答案给学习问题的加强。

英文摘要：

A novel method was designed to solve reinforcement learning problems with artificial potential field. Firstly a reinforcement learning problem was transferred to a path planning problem by using artificial potential field（APF）, which was a very appropriate method to model a reinforcement learning problem. Secondly, a new APF algorithm was proposed to overcome the local minimum problem in the potential field methods with a virtual water-flow concept. The performance of this new method was tested by a gridworld problem named as key and door maze. The experimental results show that within 45 trials, good and deterministic policies are found in almost all simulations. In comparison with WIERING＇s HQ-learning system which needs 20 000 trials for stable solution, the proposed new method can obtain optimal and stable policy far more quickly than HQ-learning. Therefore, the new method is simple and effective to give an optimal solution to the reinforcement learning problem.