5 papers
Multivariate Time Series Forecasting with Hybrid Euclidean-SPD Manifold Graph Neural Networks
Yong Fang, Na Li, Hangguan Shan +4
Multivariate Time Series (MTS) forecasting plays a vital role in various real-world applications, such as traffic management and predictive maintenance. Existing approaches typical…
SHAP-Guided Kernel Actor-Critic for Explainable Reinforcement Learning
Na Li, Hangguan Shan, Wei Ni +2
Actor-critic (AC) methods are a cornerstone of reinforcement learning (RL) but offer limited interpretability. Current explainable RL methods seldom use state attributions to assis…
Learning Causal States Under Partial Observability and Perturbation
Na Li, Hangguan Shan, Wei Ni +3
A critical challenge for reinforcement learning (RL) is making decisions based on incomplete and noisy observations, especially in perturbed and partially observable Markov decisio…
Sample-Efficient Tabular Self-Play for Offline Robust Reinforcement Learning
Na Li, Zewu Zheng, Wei Ni +3
Multi-agent reinforcement learning (MARL), as a thriving field, explores how multiple agents independently make decisions in a shared dynamic environment. Due to environmental unce…
Provable Memory Efficient Self-Play Algorithm for Model-free Reinforcement Learning
Na Li, Yuchen Jiao, Hangguan Shan +1
The thriving field of multi-agent reinforcement learning (MARL) studies how a group of interacting agents make decisions autonomously in a shared dynamic environment. Existing theo…