1 citations · 4 across the 12 of their papers we have counts for
5 papers · 1 filter
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…
Federated Learning Resilient to Byzantine Attacks and Data Heterogeneity
Shiyuan Zuo, Xingrun Yan, Rongfei Fan +4
This paper addresses federated learning (FL) in the context of malicious Byzantine attacks and data heterogeneity. We introduce a novel Robust Average Gradient Algorithm (RAGA), wh…