Abstract
Planning an obstacle-free optimal path presents great challenges for mobile robot applications, the deep deterministic policy gradient (DDPG) algorithm offers an effective solution. However, when the original DDPG is applied to robot path planning, there remains many problems such as inefficient learning and slow convergence that can adversely affect the ability to acquire optimal path. In response to these concerns, we propose an innovative framework named dueling deep deterministic policy gradient (D-DDPG) in this paper. First of all, we integrate the dueling network into the critic network to improve the estimation accuracy of Q-value. Furthermore, we design a novel reward function by combining the cosine distance with the Euclidean distance to improve learning efficiency. Our proposed model is validated by several experiments conducted in the simulation platform Gazebo. Experiments results demonstrate that our proposed model has the better path planning capability even in the unknown environment.
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This work is supported by the National Natural Science Foundation of China (61976127).
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Zhao, P., Zheng, J., Zhou, Q., Lyu, C., Lyu, L. (2021). A Dueling-DDPG Architecture for Mobile Robots Path Planning Based on Laser Range Findings. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13031. Springer, Cham. https://doi.org/10.1007/978-3-030-89188-6_12
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