Search Results for author: Zihan Ding

Found 22 papers, 11 papers with code

Efficient Reinforcement Learning Development with RLzoo

1 code implementation18 Sep 2020 Zihan Ding, Tianyang Yu, Yanhua Huang, Hongming Zhang, Guo Li, Quancheng Guo, Luo Mai, Hao Dong

RLzoo provides developers with (i) high-level yet flexible APIs for prototyping DRL agents, and further customising the agents for best performance, (ii) a model zoo where users can import a wide range of DRL agents and easily compare their performance, and (iii) an algorithm that can automatically construct DRL agents with custom components (which are critical to improve agent's performance in custom applications).

reinforcement-learning Reinforcement Learning (RL)

Object-Aware Distillation Pyramid for Open-Vocabulary Object Detection

1 code implementation CVPR 2023 Luting Wang, Yi Liu, Penghui Du, Zihan Ding, Yue Liao, Qiaosong Qi, Biaolong Chen, Si Liu

When extracting object knowledge from PVLMs, the former adaptively transforms object proposals and adopts object-aware mask attention to obtain precise and complete knowledge of objects.

Object Open Vocabulary Object Detection

A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games

2 code implementations18 Jul 2022 Zihan Ding, DiJia Su, Qinghua Liu, Chi Jin

This paper proposes new, end-to-end deep reinforcement learning algorithms for learning two-player zero-sum Markov games.

Atari Games Q-Learning

CDT: Cascading Decision Trees for Explainable Reinforcement Learning

1 code implementation15 Nov 2020 Zihan Ding, Pablo Hernandez-Leal, Gavin Weiguang Ding, Changjian Li, Ruitong Huang

As a second contribution our study reveals limitations of explaining black-box policies via imitation learning with tree-based explainable models, due to its inherent instability.

Explainable Models Imitation Learning +3

Consistency Models as a Rich and Efficient Policy Class for Reinforcement Learning

1 code implementation29 Sep 2023 Zihan Ding, Chi Jin

We propose to apply the consistency model as an efficient yet expressive policy representation, namely consistency policy, with an actor-critic style algorithm for three typical RL settings: offline, offline-to-online and online.

Image Generation Offline RL +1

TGAN: Deep Tensor Generative Adversarial Nets for Large Image Generation

1 code implementation28 Jan 2019 Zihan Ding, Xiao-Yang Liu, Miao Yin, Linghe Kong

Secondly, we propose TGAN that integrates deep convolutional generative adversarial networks and tensor super-resolution in a cascading manner, to generate high-quality images from random distributions.

Dictionary Learning Image Generation +1

PPMN: Pixel-Phrase Matching Network for One-Stage Panoptic Narrative Grounding

1 code implementation11 Aug 2022 Zihan Ding, Zi-han Ding, Tianrui Hui, Junshi Huang, Xiaoming Wei, Xiaolin Wei, Si Liu

To alleviate these drawbacks, we propose a one-stage end-to-end Pixel-Phrase Matching Network (PPMN), which directly matches each phrase to its corresponding pixels instead of region proposals and outputs panoptic segmentation by simple combination.

Panoptic Segmentation Segmentation +1

Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning

1 code implementation19 Apr 2021 Jie Ren, Yewen Li, Zihan Ding, Wei Pan, Hao Dong

However, grasping distinguishable skills for some tasks with non-unique optima can be essential for further improving its learning efficiency and performance, which may lead to a multimodal policy represented as a mixture-of-experts (MOE).

reinforcement-learning Reinforcement Learning (RL)

Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game

1 code implementation3 Jun 2022 Zhiyuan Yao, Zihan Ding

A fully distributed MARL algorithm is proposed to approximate the Nash equilibrium of the game.

Fairness Management +1

Deep Reinforcement Learning for Intelligent Transportation Systems

no code implementations3 Dec 2018 Xiao-Yang Liu, Zihan Ding, Sem Borst, Anwar Walid

Intelligent Transportation Systems (ITSs) are envisioned to play a critical role in improving traffic flow and reducing congestion, which is a pervasive issue impacting urban areas around the globe.

Management reinforcement-learning +1

Crossing The Gap: A Deep Dive into Zero-Shot Sim-to-Real Transfer for Dynamics

no code implementations15 Aug 2020 Eugene Valassakis, Zihan Ding, Edward Johns

Zero-shot sim-to-real transfer of tasks with complex dynamics is a highly challenging and unsolved problem.

DROID: Minimizing the Reality Gap using Single-Shot Human Demonstration

no code implementations22 Feb 2021 Ya-Yen Tsai, Hui Xu, Zihan Ding, Chong Zhang, Edward Johns, Bidan Huang

One of the main challenges of transferring the policy learned in a simulated environment to real world, is the discrepancy between the dynamics of the two environments.

Robotics

Collaborative Spatial-Temporal Modeling for Language-Queried Video Actor Segmentation

no code implementations CVPR 2021 Tianrui Hui, Shaofei Huang, Si Liu, Zihan Ding, Guanbin Li, Wenguan Wang, Jizhong Han, Fei Wang

Though 3D convolutions are amenable to recognizing which actor is performing the queried actions, it also inevitably introduces misaligned spatial information from adjacent frames, which confuses features of the target frame and yields inaccurate segmentation.

feature selection Referring Expression Segmentation

Reinforced Workload Distribution Fairness

no code implementations29 Oct 2021 Zhiyuan Yao, Zihan Ding, Thomas Heide Clausen

Network load balancers are central components in data centers, that distributes workloads across multiple servers and thereby contribute to offering scalable services.

Fairness Reinforcement Learning (RL)

Multi-Agent Reinforcement Learning for Network Load Balancing in Data Center

no code implementations27 Jan 2022 Zhiyuan Yao, Zihan Ding, Thomas Clausen

This paper presents the network load balancing problem, a challenging real-world task for multi-agent reinforcement learning (MARL) methods.

Multi-agent Reinforcement Learning reinforcement-learning +1

Survey of Consciousness Theory from Computational Perspective

no code implementations18 Sep 2023 Zihan Ding, Xiaoxi Wei, Yidan Xu

Human consciousness has been a long-lasting mystery for centuries, while machine intelligence and consciousness is an arduous pursuit.

Enriching Phrases with Coupled Pixel and Object Contexts for Panoptic Narrative Grounding

no code implementations2 Nov 2023 Tianrui Hui, Zihan Ding, Junshi Huang, Xiaoming Wei, Xiaolin Wei, Jiao Dai, Jizhong Han, Si Liu

Panoptic narrative grounding (PNG) aims to segment things and stuff objects in an image described by noun phrases of a narrative caption.

Object

Diffusion World Model

no code implementations5 Feb 2024 Zihan Ding, Amy Zhang, Yuandong Tian, Qinqing Zheng

We introduce Diffusion World Model (DWM), a conditional diffusion model capable of predicting multistep future states and rewards concurrently.

D4RL Q-Learning

V2X-PC: Vehicle-to-everything Collaborative Perception via Point Cluster

no code implementations25 Mar 2024 Si Liu, Zihan Ding, Jiahui Fu, Hongyu Li, Siheng Chen, Shifeng Zhang, Xu Zhou

The point cluster inherently preserves object information while packing messages, with weak relevance to the collaboration range, and supports explicit structure modeling.

Object

Cannot find the paper you are looking for? You can Submit a new open access paper.