关键词: 深度双[Q]网络（DDQN）, 竞争网络结构, 重采样优选机制, 玻尔兹曼分布, [&epsilon, ]-greedy策略

Abstract: To solve the problem of slow convergence under the basic deep [Q]-Network with which the robot explores the complex and unknown environment, an improved deep double [Q] network algorithm （Improved Dueling Deep Double [Q]-Network, IDDDQN） based on dueling network structure is put forward. The mobile robot can estimate the state-action value function of its three actions through the improved DDQN network, update the network parameters and get the corresponding [Q] value through the training. With the combination of Boltzmann and [ε]-greedy adopted, the mobile robot chooses an optimal action, and reaches the next observation. It can also store the data into experience replay memory through network learning, and train the network with mini-batch data. According to the experiment results, the mobile robot using IDDDQN can quickly adapt to the unknown environment, the convergence speed of IDDDQN is improved, the success rate of reaching the target position adds up to more than three times, and the optimal path can also be gained in an unknown complex environment.

Key words: Deep Double [Q]-Network（DDQN）, dueling network, resample experience replay memory, Boltzmann distribution, [ε]-greedy policy