Abstract
In this paper, we propose a deep reinforcement learning network using dueling and bottleneck structure to improve the task completion rate and computational efficiency of a robot arm control. The bottleneck structure applied to the neural network reduces the number of parameters and the amount of computation by adding 1 * 1 convolution and 3 * 3 convolution to the output layer. In addition, by applying the dueling structure to the neural network and dividing the function into an advantage function and a value function, it prevents the bad action selection that can occur in existing Q learning and reduces the variance of Q value, thereby improving learning stability and estimation of the agent
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