Undirected Networks
Counterfactual Conservative Q Learning for Offline Multi-agent Reinforcement Learning Jianzhun Shao, Y un Qu
MARL in real scenarios is still challenging due to the same safety and efficiency concerns in single-agent setting, then it is worth conducting investigation for offline RL in multi-agent setting.
Optimal Convergence Rate for Exact Policy Mirror Descent in Discounted Markov Decision Processes
Our study of the convergence of PMD avoids the use of the performance difference lemma, which leads to a direct analysis of independent interest.
ODE-based Recurrent Model-free Reinforcement Learning for POMDPs
In partially observable (PO) environments, how to infer unseen information from raw observations puzzled the agents.
Sample Complexity of Goal-Conditioned Hierarchical Reinforcement Learning
HRL lead to improved sample complexity, and how much of an improvement can it provide? Theoretical work on sample-complexity bound in Machine Learning has been integral to the development of the field.
