48 citations · 107 across the 13 of their papers we have counts for
13 papers
A Meta Reinforcement Learning Approach for Predictive Autoscaling in the Cloud
Siqiao Xue, Chao Qu, Xiaoming Shi +11
Predictive autoscaling (autoscaling with workload forecasting) is an important mechanism that supports autonomous adjustment of computing resources in accordance with fluctuating w…
Bellman Meets Hawkes: Model-Based Reinforcement Learning via Temporal Point Processes
Chao Qu, Xiaoyu Tan, Siqiao Xue +3
We consider a sequential decision making problem where the agent faces the environment characterized by the stochastic discrete events and seeks an optimal intervention policy such…
Model Embedding Model-Based Reinforcement Learning
Xiaoyu Tan, Chao Qu, Junwu Xiong +1
Model-based reinforcement learning (MBRL) has shown its advantages in sample-efficiency over model-free reinforcement learning (MFRL). Despite the impressive results it achieves, i…
Variational Policy Propagation for Multi-agent Reinforcement Learning
Chao Qu, Hui Li, Chang Liu +6
We propose a \emph{collaborative} multi-agent reinforcement learning algorithm named variational policy propagation (VPP) to learn a \emph{joint} policy through the interactions ov…
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning
Chao Qu, Shie Mannor, Huan Xu +3
We consider the networked multi-agent reinforcement learning (MARL) problem in a fully decentralized setting, where agents learn to coordinate to achieve the joint success. This pr…
Projection-Free Algorithms in Statistical Estimation
Yan Li, Chao Qu, Huan Xu
Frank-Wolfe algorithm (FW) and its variants have gained a surge of interests in machine learning community due to its projection-free property. Recently people have reduced the gra…