ART2 neural network interacting with environment

It is common to train a neural network by using samples so that it can realize the required input–output characteristics. However, to obtain such samples is difficult or even impossible in some cases. This paper proposes the use of on-line reinforcement learning (RL) algorithms to train adaptive-resonance-theory-based (ART2) neural networks through interaction with environments, namely RL-ART2 neural network. By utilizing its adaptation ability to a dynamic environment, RL is able to evaluate and select ART2 classification patterns without training samples. The connection weights can be automatically modified according to the running effect evaluation of classification pattern of neural networks. The proposed novel RL-ART2 neural network is applied to implement the collaboration movement of mobile robots. Simulation results are presented to demonstrate the feasibility and performance of the proposed algorithm.