Search Results for author: Cheng Wu

Found 8 papers, 4 papers with code

HybridGNN: Learning Hybrid Representation in Multiplex Heterogeneous Networks

no code implementations3 Aug 2022 Tiankai Gu, Chaokun Wang, Cheng Wu, Jingcao Xu, Yunkai Lou, Changping Wang, Kai Xu, Can Ye, Yang song

One of the most important tasks in recommender systems is to predict the potential connection between two nodes under a specific edge type (i. e., relationship).

Recommendation Systems

Temporal-Spatial Causal Interpretations for Vision-Based Reinforcement Learning

no code implementations6 Dec 2021 Wenjie Shi, Gao Huang, Shiji Song, Cheng Wu

TSCI model builds on the formulation of temporal causality, which reflects the temporal causal relations between sequential observations and decisions of RL agent.

Causal Discovery Decision Making +1

Regularizing Deep Networks with Semantic Data Augmentation

1 code implementation21 Jul 2020 Yulin Wang, Gao Huang, Shiji Song, Xuran Pan, Yitong Xia, Cheng Wu

The proposed method is inspired by the intriguing property that deep networks are effective in learning linearized features, i. e., certain directions in the deep feature space correspond to meaningful semantic transformations, e. g., changing the background or view angle of an object.

Data Augmentation

Self-Supervised Discovering of Interpretable Features for Reinforcement Learning

1 code implementation16 Mar 2020 Wenjie Shi, Gao Huang, Shiji Song, Zhuoyuan Wang, Tingyu Lin, Cheng Wu

Deep reinforcement learning (RL) has recently led to many breakthroughs on a range of complex control tasks.

Atari Games Decision Making +1

Implicit Semantic Data Augmentation for Deep Networks

1 code implementation NeurIPS 2019 Yulin Wang, Xuran Pan, Shiji Song, Hong Zhang, Cheng Wu, Gao Huang

Our work is motivated by the intriguing property that deep networks are surprisingly good at linearizing features, such that certain directions in the deep feature space correspond to meaningful semantic transformations, e. g., adding sunglasses or changing backgrounds.

Image Augmentation

Soft Policy Gradient Method for Maximum Entropy Deep Reinforcement Learning

no code implementations7 Sep 2019 Wenjie Shi, Shiji Song, Cheng Wu

Then, we present an off-policy actor-critic, model-free maximum entropy deep RL algorithm called deep soft policy gradient (DSPG) by combining soft policy gradient with soft Bellman equation.

reinforcement-learning

Multi Pseudo Q-learning Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles

no code implementations7 Sep 2019 Wenjie Shi, Shiji Song, Cheng Wu, C. L. Philip Chen

Different from existing policy gradient methods which employ single actor-critic but cannot realize satisfactory tracking control accuracy and stable learning, our proposed algorithm can achieve high-level tracking control accuracy of AUVs and stable learning by applying a hybrid actors-critics architecture, where multiple actors and critics are trained to learn a deterministic policy and action-value function, respectively.

Policy Gradient Methods Q-Learning

Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning

1 code implementation NeurIPS 2019 Wenjie Shi, Shiji Song, Hui Wu, Ya-Chu Hsu, Cheng Wu, Gao Huang

To tackle this problem, we propose a general acceleration method for model-free, off-policy deep RL algorithms by drawing the idea underlying regularized Anderson acceleration (RAA), which is an effective approach to accelerating the solving of fixed point problems with perturbations.

reinforcement-learning

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