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Reinforcement Fuzzy Tree: A Method extracting Rules from Reinforcement Learning Models

Reinforcement learning methods are more and more popular in machine learning fields, and have achieved outstanding breakthroughs in various applications combined with deep convolution neural networks. Deep convolution neural networks have excellent fitting abilities and promote the prosperity of machine learning. However, we want to look inside the model and get some rules sometime, but it is a hard task for deep convolution neural networks and reinforcement learning methods now. Our reinforcement fuzzy tree model unites reinforcement learning methods, deep convolution neural networks and decision tree, could extract explicit rules and keep excellent performance at the same time. Our paper show that models based on reinforcement learning and deep convolution neural networks could be redundancy sometime, but the extracted rules could be simpler than the model we constructing and keep the same performances.