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第43届中国控制会议

作者： penghang shuai Haipeng Li Yongkang Luo Liangwei Deng The Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences

Reinforcement learning control has gained increasing attention in the field of biomimetic robotic fish, due to its advantage of universal applicability without prior knowledge of dynamic modeling. In this paper, we review the research work on the application of reinforcement learning in the field of robotic fish. We present and discuss the general model of applying reinforcement learning approaches to robotic fish control, such as the design of environment, state, reward and the selection of reinforcement learning algorithm. Furthermore, we propose to divide the typical tasks of reinforcement learning applied in robotic fish into single control and swarm control, and review the recent advancements separately.

关键词： Reinforcement Learning Control Robotic Fish Single Control Swarm Control

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QUANTIZATION AND TRAINING OF LOW BIT-WIDTH CONVOLUTIONAL NEURAL NETWORKS FOR OBJECT DETECTION

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Journal of Computational Mathematics 2019年第3期37卷 349-359页

作者： penghang Yin shuai Zhang Yingyong Qi Jack Xin Department of Mathematics University of CaliforniaLos AngelesCA 90095USA Department of Mathematics University of CaliforniaIrvineCA 92697USA

We presen t LBW-Net,an efficient optimization based method for qua nt ization and training of the low bit-width convolutional neural networks(CNNs).Specifically,we quantize the weights to zero or powers of 2 by minimizing the Euclidean distance between full-precision weights and quantized weights during backpropagation(weight learning).We characterize the combinatorial nature of the low bit-width quantization *** 2-bit(ternary)CNNs,the quantization of N weights can be done by an exact formula in O(N log N)*** the bit-width is 3 and above,we further propose a semi-analytical thresholding scheme with a single free parameter for quantization that is computationally *** free parameter is further determined by network retraining and object detection *** LBW-Net has several desirable advantages over full-precision CNNs,including considerable memory savings,energy efficiency,and faster *** experiments on PASCAL VOC dataset show that compared with its 32-bit floating-point counterpart,the performance of the 6-bit LBW-Net is nearly lossless in the object detection tasks,and can even do better in real world visual scenes,while empirically enjoying more than 4× faster deployment.

关键词： Quantization Low bit width deep neural networks Exact and approximate analytical formulas Network training Object detection

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