151 citations · 401 across the 21 of their papers we have counts for
21 papers
Divergence-Augmented Policy Optimization
Qing Wang, Yingru Li, Jiechao Xiong +1
In deep reinforcement learning, policy optimization methods need to deal with issues such as function approximation and the reuse of off-policy data. Standard policy gradient metho…
Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning
Hanlin Yang, Jian Yao, Weiming Liu +13
Recovering a spectrum of diverse policies from a set of expert trajectories is an important research topic in imitation learning. After determining a latent style for a trajectory,…
TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement Learning
Peng Sun, Jiechao Xiong, Lei Han +5
Competitive Self-Play (CSP) based Multi-Agent Reinforcement Learning (MARL) has shown phenomenal breakthroughs recently. Strong AIs are achieved for several benchmarks, including D…
TStarBot-X: An Open-Sourced and Comprehensive Study for Efficient League Training in StarCraft II Full Game
Lei Han, Jiechao Xiong, Peng Sun +8
StarCraft, one of the most difficult esport games with long-standing history of professional tournaments, has attracted generations of players and fans, and also, intense attention…
Zeroth-Order Supervised Policy Improvement
Hao Sun, Ziping Xu, Yuhang Song +4
Policy gradient (PG) algorithms have been widely used in reinforcement learning (RL). However, PG algorithms rely on exploiting the value function being learned with the first-orde…
Arena: a toolkit for Multi-Agent Reinforcement Learning
Qing Wang, Jiechao Xiong, Lei Han +5
We introduce Arena, a toolkit for multi-agent reinforcement learning (MARL) research. In MARL, it usually requires customizing observations, rewards and actions for each agent, cha…
Parametrized Deep Q-Networks Learning: Reinforcement Learning with Discrete-Continuous Hybrid Action Space
Jiechao Xiong, Qing Wang, Zhuoran Yang +7
Most existing deep reinforcement learning (DRL) frameworks consider either discrete action space or continuous action space solely. Motivated by applications in computer games, we…
TStarBots: Defeating the Cheating Level Builtin AI in StarCraft II in the Full Game
Peng Sun, Xinghai Sun, Lei Han +8
Starcraft II (SC2) is widely considered as the most challenging Real Time Strategy (RTS) game. The underlying challenges include a large observation space, a huge (continuous and i…
A Margin-based MLE for Crowdsourced Partial Ranking
Qianqian Xu, Jiechao Xiong, Xinwei Sun +4
A preference order or ranking aggregated from pairwise comparison data is commonly understood as a strict total order. However, in real-world scenarios, some items are intrinsicall…
From Social to Individuals: a Parsimonious Path of Multi-level Models for Crowdsourced Preference Aggregation
Qianqian Xu, Jiechao Xiong, Xiaochun Cao +2
In crowdsourced preference aggregation, it is often assumed that all the annotators are subject to a common preference or social utility function which generates their comparison b…
HodgeRank with Information Maximization for Crowdsourced Pairwise Ranking Aggregation
Qianqian Xu, Jiechao Xiong, Xi Chen +2
Recently, crowdsourcing has emerged as an effective paradigm for human-powered large scale problem solving in various domains. However, task requester usually has a limited amount…
Stochastic Non-convex Ordinal Embedding with Stabilized Barzilai-Borwein Step Size
Ke Ma, Jinshan Zeng, Jiechao Xiong +4
Learning representation from relative similarity comparisons, often called ordinal embedding, gains rising attention in recent years. Most of the existing methods are batch methods…
Exploring Outliers in Crowdsourced Ranking for QoE
Qianqian Xu, Ming Yan, Chendi Huang +3
Outlier detection is a crucial part of robust evaluation for crowdsourceable assessment of Quality of Experience (QoE) and has attracted much attention in recent years. In this pap…
Boosting with Structural Sparsity: A Differential Inclusion Approach
Chendi Huang, Xinwei Sun, Jiechao Xiong +1
Boosting as gradient descent algorithms is one popular method in machine learning. In this paper a novel Boosting-type algorithm is proposed based on restricted gradient descent wi…
Parsimonious Mixed-Effects HodgeRank for Crowdsourced Preference Aggregation
Qianqian Xu, Jiechao Xiong, Xiaochun Cao +1
In crowdsourced preference aggregation, it is often assumed that all the annotators are subject to a common preference or utility function which generates their comparison behavior…
False Discovery Rate Control and Statistical Quality Assessment of Annotators in Crowdsourced Ranking
Qianqian Xu, Jiechao Xiong, Xiaochun Cao +1
With the rapid growth of crowdsourcing platforms it has become easy and relatively inexpensive to collect a dataset labeled by multiple annotators in a short time. However due to t…
A Tutorial on Libra: R package for the Linearized Bregman Algorithm in High Dimensional Statistics
Jiechao Xiong, Feng Ruan, Yuan Yao
The R package, Libra, stands for the LInearized BRegman Al- gorithm in high dimensional statistics. The Linearized Bregman Algorithm is a simple iterative procedure to generate spa…
Analysis of Crowdsourced Sampling Strategies for HodgeRank with Sparse Random Graphs
Braxton Osting, Jiechao Xiong, Qianqian Xu +1
Crowdsourcing platforms are now extensively used for conducting subjective pairwise comparison studies. In this setting, a pairwise comparison dataset is typically gathered via ran…
Robust Subjective Visual Property Prediction from Crowdsourced Pairwise Labels
Yanwei Fu, Timothy M. Hospedales, Tao Xiang +4
The problem of estimating subjective visual properties from image and video has attracted increasing interest. A subjective visual property is useful either on its own (e.g. image…
Evaluating Visual Properties via Robust HodgeRank
Qianqian Xu, Jiechao Xiong, Xiaochun Cao +2
Nowadays, how to effectively evaluate visual properties has become a popular topic for fine-grained visual comprehension. In this paper we study the problem of how to estimate such…
Sparse Recovery via Differential Inclusions
Stanley Osher, Feng Ruan, Jiechao Xiong +2
In this paper, we recover sparse signals from their noisy linear measurements by solving nonlinear differential inclusions, which is based on the notion of inverse scale space (ISS…