3 papers
cs.LG2025
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…
cs.LG2025
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…
cs.LG2025
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…