11 citations · 29 across the 10 of their papers we have counts for
10 papers
Scalable Multi-agent Covering Option Discovery based on Kronecker Graphs
Jiayu Chen, Jingdi Chen, Tian Lan +1
Covering skill (a.k.a., option) discovery has been developed to improve the exploration of RL in single-agent scenarios with sparse reward signals, through connecting the most dist…
Multi-task Hierarchical Adversarial Inverse Reinforcement Learning
Jiayu Chen, Dipesh Tamboli, Tian Lan +1
Multi-task Imitation Learning (MIL) aims to train a policy capable of performing a distribution of tasks based on multi-task expert demonstrations, which is essential for general-p…
Learning-based Two-tiered Online Optimization of Region-wide Datacenter Resource Allocation
Chang-Lin Chen, Hanhan Zhou, Jiayu Chen +8
Online optimization of resource management for large-scale data centers and infrastructures to meet dynamic capacity reservation demands and various practical constraints (e.g., fe…
Hierarchical Deep Counterfactual Regret Minimization
Jiayu Chen, Xudong Wu, Zhekai Wang +1
Imperfect Information Games (IIGs) are used to model games under uncertainty or lack complete information. Counterfactual Regret Minimization (CFR) is one of the most successful fa…
Asynchronous Multi-Agent Reinforcement Learning for Efficient Real-Time Multi-Robot Cooperative Exploration
Chao Yu, Xinyi Yang, Jiaxuan Gao +8
We consider the problem of cooperative exploration where multiple robots need to cooperatively explore an unknown region as fast as possible. Multi-agent reinforcement learning (MA…
A Unified Algorithm Framework for Unsupervised Discovery of Skills based on Determinantal Point Process
Jiayu Chen, Vaneet Aggarwal, Tian Lan
Learning rich skills under the option framework without supervision of external rewards is at the frontier of reinforcement learning research. Existing works mainly fall into two d…
Multi-agent Deep Covering Skill Discovery
Jiayu Chen, Marina Haliem, Tian Lan +1
The use of skills (a.k.a., options) can greatly accelerate exploration in reinforcement learning, especially when only sparse reward signals are available. While option discovery m…
Option-Aware Adversarial Inverse Reinforcement Learning for Robotic Control
Jiayu Chen, Tian Lan, Vaneet Aggarwal
Hierarchical Imitation Learning (HIL) has been proposed to recover highly-complex behaviors in long-horizon tasks from expert demonstrations by modeling the task hierarchy with the…
Learning Multi-agent Skills for Tabular Reinforcement Learning using Factor Graphs
Jiayu Chen, Jingdi Chen, Tian Lan +1
Covering skill (a.k.a., option) discovery has been developed to improve the exploration of reinforcement learning in single-agent scenarios with sparse reward signals, through conn…
DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight Delivery
Jiayu Chen, Abhishek K. Umrawal, Tian Lan +1
With the freight delivery demands and shipping costs increasing rapidly, intelligent control of fleets to enable efficient and cost-conscious solutions becomes an important problem…