Combining the principles of fuzzy logic and reinforcement learning for control of dynamic systems

Reinforcement learning is a method in which agent/agents obtain a positive or negative reward to do an efficient operation. In this way, the performance will be very suitable for the systems which are naturally complicated for deriving the differential equations. This can be a good alternative to other control areas. One of the main disadvantages of this method is considering the discrete actions during it. However, many of dynamical systems couldnt be optimized by this approach. To remedy this deficiency, different approaches have emerged, including approximate methods. In this paper, fuzzy logic is used to continually optimize the operations. In this case, the reinforcement learning method sets the fuzzy control rules which are the principles of optimal control. Two approaches, stabilizing the pendulum and both of pendulum and cart are considered to control the pole- cart problem in this paper. The results show that the applied artificial intelligence can be used as a proper solution for the taken policy.