Search Results for author: Zhendong Wang

Found 14 papers, 7 papers with code

Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning

no code implementations12 Aug 2022 Zhendong Wang, Jonathan J Hunt, Mingyuan Zhou

In our approach, we learn an action-value function and we add a term maximizing action-values into the training loss of a conditional diffusion model, which results in a loss that seeks optimal actions that are near the behavior policy.

Offline RL reinforcement-learning

Probabilistic Conformal Prediction Using Conditional Random Samples

1 code implementation14 Jun 2022 Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David M. Blei

This paper proposes probabilistic conformal prediction (PCP), a predictive inference algorithm that estimates a target variable by a discontinuous predictive set.

Diffusion-GAN: Training GANs with Diffusion

2 code implementations5 Jun 2022 Zhendong Wang, Huangjie Zheng, Pengcheng He, Weizhu Chen, Mingyuan Zhou

For stable training of generative adversarial networks (GANs), injecting instance noise into the input of the discriminator is considered as a theoretically sound solution, which, however, has not yet delivered on its promise in practice.

Image Generation

Network Topology Optimization via Deep Reinforcement Learning

no code implementations19 Apr 2022 Zhuoran Li, Xing Wang, Ling Pan, Lin Zhu, Zhendong Wang, Junlan Feng, Chao Deng, Longbo Huang

A2C-GS consists of three novel components, including a verifier to validate the correctness of a generated network topology, a graph neural network (GNN) to efficiently approximate topology rating, and a DRL actor layer to conduct a topology search.

Management reinforcement-learning

Enabling Efficient Deep Convolutional Neural Network-based Sensor Fusion for Autonomous Driving

no code implementations22 Feb 2022 Xiaoming Zeng, Zhendong Wang, Yang Hu

We also propose a Layer-sharing technique in the deep layer that can achieve better accuracy with less computational overhead.

Autonomous Driving Decision Making

A Regularized Implicit Policy for Offline Reinforcement Learning

no code implementations19 Feb 2022 Shentao Yang, Zhendong Wang, Huangjie Zheng, Yihao Feng, Mingyuan Zhou

For training more effective agents, we propose a framework that supports learning a flexible yet well-regularized fully-implicit policy.

reinforcement-learning

State-Action Joint Regularized Implicit Policy for Offline Reinforcement Learning

no code implementations29 Sep 2021 Shentao Yang, Zhendong Wang, Huangjie Zheng, Mingyuan Zhou

For training more effective agents, we propose a framework that supports learning a flexible and well-regularized policy, which consists of a fully implicit policy and a regularization through the state-action visitation frequency induced by the current policy and that induced by the data-collecting behavior policy.

reinforcement-learning

Uformer: A General U-Shaped Transformer for Image Restoration

4 code implementations CVPR 2022 Zhendong Wang, Xiaodong Cun, Jianmin Bao, Wengang Zhou, Jianzhuang Liu, Houqiang Li

Powered by these two designs, Uformer enjoys a high capability for capturing both local and global dependencies for image restoration.

Deblurring Image Deblurring +6

Implicit Distributional Reinforcement Learning

3 code implementations NeurIPS 2020 Yuguang Yue, Zhendong Wang, Mingyuan Zhou

To improve the sample efficiency of policy-gradient based reinforcement learning algorithms, we propose implicit distributional actor-critic (IDAC) that consists of a distributional critic, built on two deep generator networks (DGNs), and a semi-implicit actor (SIA), powered by a flexible policy distribution.

Distributional Reinforcement Learning OpenAI Gym +1

Adaptive Correlated Monte Carlo for Contextual Categorical Sequence Generation

1 code implementation ICLR 2020 Xinjie Fan, Yizhe Zhang, Zhendong Wang, Mingyuan Zhou

To stabilize this method, we adapt to contextual generation of categorical sequences a policy gradient estimator, which evaluates a set of correlated Monte Carlo (MC) rollouts for variance control.

Image Captioning Program Synthesis

Thompson Sampling via Local Uncertainty

1 code implementation ICML 2020 Zhendong Wang, Mingyuan Zhou

Variational inference is used to approximate the posterior of the local variable, and semi-implicit structure is further introduced to enhance its expressiveness.

Decision Making Multi-Armed Bandits +1

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