Search Results for author: Yufeng Yuan

Found 8 papers, 3 papers with code

Asynchronous Reinforcement Learning for Real-Time Control of Physical Robots

1 code implementation23 Mar 2022 Yufeng Yuan, A. Rupam Mahmood

An oft-ignored challenge of real-world reinforcement learning is that the real world does not pause when agents make learning updates.

reinforcement-learning

Maximum Entropy Population-Based Training for Zero-Shot Human-AI Coordination

1 code implementation22 Dec 2021 Rui Zhao, Jinming Song, Yufeng Yuan, Hu Haifeng, Yang Gao, Yi Wu, Zhongqian Sun, Yang Wei

We study the problem of training a Reinforcement Learning (RL) agent that is collaborative with humans without using any human data.

Receptive Multi-granularity Representation for Person Re-Identification

no code implementations31 Aug 2020 Guanshuo Wang, Yufeng Yuan, Jiwei Li, Shiming Ge, Xi Zhou

Current stripe-based feature learning approaches have delivered impressive accuracy, but do not make a proper trade-off between diversity, locality, and robustness, which easily suffers from part semantic inconsistency for the conflict between rigid partition and misalignment.

Person Re-Identification

Relation-Aware Pyramid Network (RapNet) for temporal action proposal

no code implementations9 Aug 2019 Jialin Gao, Zhixiang Shi, Jiani Li, Yufeng Yuan, Jiwei Li, Xi Zhou

In this technical report, we describe our solution to temporal action proposal (task 1) in ActivityNet Challenge 2019.

Meta Reinforcement Learning with Distribution of Exploration Parameters Learned by Evolution Strategies

no code implementations29 Dec 2018 Yiming Shen, Kehan Yang, Yufeng Yuan, Simon Cheng Liu

In this paper, we propose a novel meta-learning method in a reinforcement learning setting, based on evolution strategies (ES), exploration in parameter space and deterministic policy gradients.

Meta-Learning Meta Reinforcement Learning +1

Continual Match Based Training in Pommerman: Technical Report

no code implementations18 Dec 2018 Peng Peng, Liang Pang, Yufeng Yuan, Chao GAO

We show in the experiments that Pommerman is a perfect environment for studying continual learning, and the agent can improve its performance by continually learning new skills without forgetting the old ones.

Continual Learning

Learning Discriminative Features with Multiple Granularities for Person Re-Identification

14 code implementations4 Apr 2018 Guanshuo Wang, Yufeng Yuan, Xiong Chen, Jiwei Li, Xi Zhou

Instead of learning on semantic regions, we uniformly partition the images into several stripes, and vary the number of parts in different local branches to obtain local feature representations with multiple granularities.

Ranked #3 on Person Re-Identification on SYSU-30k (using extra training data)

Person Re-Identification Re-Ranking

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