19 citations · 60 across the 23 of their papers we have counts for
10 papers · 1 filter
Statistically Efficient Variance Reduction with Double Policy Estimation for Off-Policy Evaluation in Sequence-Modeled Reinforcement Learning
Hanhan Zhou, Tian Lan, Vaneet Aggarwal
Offline reinforcement learning aims to utilize datasets of previously gathered environment-action interaction records to learn a policy without access to the real environment. Rece…
RGMComm: Return Gap Minimization via Discrete Communications in Multi-Agent Reinforcement Learning
Jingdi Chen, Tian Lan, Carlee Joe-Wong
Communication is crucial for solving cooperative Multi-Agent Reinforcement Learning tasks in partially observable Markov Decision Processes. Existing works often rely on black-box…
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…
MM-DAG: Multi-task DAG Learning for Multi-modal Data -- with Application for Traffic Congestion Analysis
Tian Lan, Ziyue Li, Zhishuai Li +6
This paper proposes to learn Multi-task, Multi-modal Direct Acyclic Graphs (MM-DAGs), which are commonly observed in complex systems, e.g., traffic, manufacturing, and weather syst…
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…
FERN: Leveraging Graph Attention Networks for Failure Evaluation and Robust Network Design
Chenyi Liu, Vaneet Aggarwal, Tian Lan +4
Robust network design, which aims to guarantee network availability under various failure scenarios while optimizing performance/cost objectives, has received significant attention…
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…
MAC-PO: Multi-Agent Experience Replay via Collective Priority Optimization
Yongsheng Mei, Hanhan Zhou, Tian Lan +2
Experience replay is crucial for off-policy reinforcement learning (RL) methods. By remembering and reusing the experiences from past different policies, experience replay signific…
ReMIX: Regret Minimization for Monotonic Value Function Factorization in Multiagent Reinforcement Learning
Yongsheng Mei, Hanhan Zhou, Tian Lan
Value function factorization methods have become a dominant approach for cooperative multiagent reinforcement learning under a centralized training and decentralized execution para…
Scalability Bottlenecks in Multi-Agent Reinforcement Learning Systems
Kailash Gogineni, Peng Wei, Tian Lan +1
Multi-Agent Reinforcement Learning (MARL) is a promising area of research that can model and control multiple, autonomous decision-making agents. During online training, MARL algor…