An Improved Reinforcement Q-Learning Method with BP Neural Networks in Robot Soccer

摘要

In traditional reinforcement Q-Learning method, there exists two problems: difficulty of dividing the state information, complexity of extreme large dimension input. To solve these two problems, this paper proposed an improved reinforcement Q-Learning method with BP neutral network. In this method, the large Q table is replaced by a BP neural network. Continuous environmental information is the input. The Q value is the output. The Q value and weight of the network are also adjusted by the action rewards. This paper presents an algorithm for single agent's action selection. Simulation shows proposed method is more stable and applicable for the agent's strategy selection.