103 citations · 864 across the 68 of their papers we have counts for
9 papers · 2 filters
A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning
Xidong Feng, Bo Liu, Jie Ren +5
Gradient-based Meta-RL (GMRL) refers to methods that maintain two-level optimisation procedures wherein the outer-loop meta-learner guides the inner-loop gradient-based reinforceme…
Offline Pre-trained Multi-Agent Decision Transformer: One Big Sequence Model Tackles All SMAC Tasks
Linghui Meng, Muning Wen, Yaodong Yang +7
Offline reinforcement learning leverages previously-collected offline datasets to learn optimal policies with no necessity to access the real environment. Such a paradigm is also d…
A Game-Theoretic Approach for Improving Generalization Ability of TSP Solvers
Chenguang Wang, Yaodong Yang, Oliver Slumbers +4
In this paper, we introduce a two-player zero-sum framework between a trainable \emph{Solver} and a \emph{Data Generator} to improve the generalization ability of deep learning-bas…
DESTA: A Framework for Safe Reinforcement Learning with Markov Games of Intervention
David Mguni, Usman Islam, Yaqi Sun +7
Reinforcement learning (RL) involves performing exploratory actions in an unknown system. This can place a learning agent in dangerous and potentially catastrophic system states. C…
Online Markov Decision Processes with Non-oblivious Strategic Adversary
Le Cong Dinh, David Henry Mguni, Long Tran-Thanh +2
We study a novel setting in Online Markov Decision Processes (OMDPs) where the loss function is chosen by a non-oblivious strategic adversary who follows a no-external regret algor…
Revisiting the Characteristics of Stochastic Gradient Noise and Dynamics
Yixin Wu, Rui Luo, Chen Zhang +2
In this paper, we characterize the noise of stochastic gradients and analyze the noise-induced dynamics during training deep neural networks by gradient-based optimizers. Specifica…