71 citations · 74 across the 6 of their papers we have counts for
6 papers
Linear-Quadratic Gaussian Games with Distributed Sparse Estimation
Tianyu Qiu, Filippos Fotiadis, Xinjie Liu +5
Linear-quadratic Gaussian games provide a framework for modeling strategic interactions in multi-agent systems, where agents must estimate system states from noisy observations whi…
Efficiently Solving Mixed-Hierarchy Games with Quasi-Policy Approximations
Hamzah Khan, Dong Ho Lee, Jingqi Li +5
Multi-robot coordination often exhibits hierarchical structure, with some robots' decisions depending on the planned behaviors of others. While game theory provides a principled fr…
A Player Selection Network for Scalable Game-Theoretic Prediction and Planning
Tianyu Qiu, Eric Ouano, Fernando Palafox +2
While game-theoretic planning frameworks are effective at modeling multi-agent interactions, they require solving large optimization problems where the number of variables increase…
Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations
Cevahir Koprulu, Po-han Li, Tianyu Qiu +5
Many continuous control problems can be formulated as sparse-reward reinforcement learning (RL) tasks. In principle, online RL methods can automatically explore the state space to…
Inferring Occluded Agent Behavior in Dynamic Games from Noise Corrupted Observations
Tianyu Qiu, David Fridovich-Keil
In mobile robotics and autonomous driving, it is natural to model agent interactions as the Nash equilibrium of a noncooperative, dynamic game. These methods inherently rely on obs…
On the Effectiveness of Sampled Softmax Loss for Item Recommendation
Jiancan Wu, Xiang Wang, Xingyu Gao +3
The learning objective plays a fundamental role to build a recommender system. Most methods routinely adopt either pointwise or pairwise loss to train the model parameters, while r…