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Ruimin Shen

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4 papers

1 author row

IJCAI Conference 2020 Conference Paper

Generating Behavior-Diverse Game AIs with Evolutionary Multi-Objective Deep Reinforcement Learning

Ruimin Shen
Yan Zheng
Jianye Hao
Zhaopeng Meng
Yingfeng Chen
Changjie Fan
Yang Liu

Generating diverse behaviors for game artificial intelligence (Game AI) has been long recognized as a challenging task in the game industry. Designing a Game AI with a satisfying behavioral characteristic (style) heavily depends on the domain knowledge and is hard to achieve manually. Deep reinforcement learning sheds light on advancing the automatic Game AI design. However, most of them focus on creating a superhuman Game AI, ignoring the importance of behavioral diversity in games. To bridge the gap, we introduce a new framework, named EMOGI, which can automatically generate desirable styles with almost no domain knowledge. More importantly, EMOGI succeeds in creating a range of diverse styles, providing behavior-diverse Game AIs. Evaluations on the Atari and real commercial games indicate that, compared to existing algorithms, EMOGI performs better in generating diverse behaviors and significantly improves the efficiency of Game AI design.

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IJCAI Conference 2020 Conference Paper

GestureDet: Real-time Student Gesture Analysis with Multi-dimensional Attention-based Detector

Rui Zheng
Fei Jiang
Ruimin Shen

Students’ gestures, hand-raising, stand-up, and sleeping, indicates the engagement of students in classrooms and partially reflects teaching quality. Therefore, fast and automatically recognizing these gestures are of great importance. Due to limited computational resources in primary and secondary schools, we propose a real-time student behavior detector based on light-weight MobileNetV2-SSD to reduce the dependency of GPUs. Firstly, we build a large-scale corpus from real schools to capture various behavior gestures. Based on such a corpus, we transfer the gesture recognition task into object detections. Secondly, we design a multi-dimensional attention-based detector, named GestureDet, for real-time and accurate gesture analysis. The multi-dimensional attention mechanisms simultaneously consider all the dimensions of the training set, aiming to pay more attention to discriminative features and samples that are important for the final performance. Specifically, the spatial attention is constructed with stacked dilated convolution layers to generate a soft and learnable mask for re-weighting foreground and background features; the channel attention introduces the context modeling and squeeze-and-excitation module to focus on discriminative features; the batch attention discriminates important samples with a new designed reweight strategy. Experimental results demonstrate the effectiveness and versatility of GestureDet, which achieves 75. 2% mAP on real student behavior dataset, and 74. 5% on public PASCAL VOC dataset at 20fps on embedding device Nvidia Jetson TX2. Code will be made publicly available.

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AAAI Conference 2018 Conference Paper

Efficient Multi-Dimensional Tensor Sparse Coding Using t-Linear Combination

Fei Jiang
Xiao-Yang Liu
Hongtao Lu
Ruimin Shen

In this paper, we propose two novel multi-dimensional tensor sparse coding (MDTSC) schemes using the t-linear combination. Based on the t-linear combination, the shifted versions of the bases are used for the data approximation, but without need to store them. Therefore, the dictionaries of the proposed schemes are more concise and the coefﬁcients have richer physical explanations. Moreover, we propose an efﬁcient alternating minimization algorithm, including the tensor coefﬁcient learning and the tensor dictionary learning, to solve the proposed problems. For the tensor coefﬁcient learning, we design a tensor-based fast iterative shrinkage algorithm. For the tensor dictionary learning, we ﬁrst divide the problem into several nearly-independent subproblems in the frequency domain, and then utilize the Lagrange dual to further reduce the number of optimization variables. Experimental results on multi-dimensional signals denoising and reconstruction (3DTSC, 4DTSC, 5DTSC) show that the proposed algorithms are more efﬁcient and outperform the state-of-theart tensor-based sparse coding models.

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AAAI Conference 2011 Conference Paper

Sparse Group Restricted Boltzmann Machines

Heng Luo
Ruimin Shen
Changyong Niu
Carsten Ullrich

Since learning in Boltzmann machines is typically quite slow, there is a need to restrict connections within hidden layers. However, the resulting states of hidden units exhibit statistical dependencies. Based on this observation, we propose using l1/l2 regularization upon the activation probabilities of hidden units in restricted Boltzmann machines to capture the local dependencies among hidden units. This regularization not only encourages hidden units of many groups to be inactive given observed data but also makes hidden units within a group compete with each other for modeling observed data. Thus, the l1/l2 regularization on RBMs yields sparsity at both the group and the hidden unit levels. We call RBMs trained with the regularizer sparse group RBMs (SGRBMs). The proposed SGRBMs are applied to model patches of natural images, handwritten digits and OCR English letters. Then to emphasize that SGRBMs can learn more discriminative features we applied SGRBMs to pretrain deep networks for classiﬁcation tasks. Furthermore, we illustrate the regularizer can also be applied to deep Boltzmann machines, which lead to sparse group deep Boltzmann machines. When adapted to the MNIST data set, a two-layer sparse group Boltzmann machine achieves an error rate of 0. 84%, which is, to our knowledge, the best published result on the permutation-invariant version of the MNIST task.

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