4 papers
Advantage-Guided Diffusion for Model-Based Reinforcement Learning
Daniele Foffano, Arvid Eriksson, David Broman +2
Model-based reinforcement learning (MBRL) with autoregressive world models suffers from compounding errors, whereas diffusion world models mitigate this by generating trajectory se…
Pareto-optimal Trade-offs Between Communication and Computation with Flexible Gradient Tracking
Yan Huang, Jinming Xu, Li Chai +2
This paper addresses distributed stochastic optimization problems under non-i.i.d. data, focusing on the inherent trade-offs between communication and computational efficiency. To…
Beyond Scaffold: A Unified Spatio-Temporal Gradient Tracking Method
Yan Huang, Jinming Xu, Jiming Chen +1
In distributed and federated learning algorithms, communication overhead is often reduced by performing multiple local updates between communication rounds. However, due to data he…
An Optimistic Gradient Tracking Method for Distributed Minimax Optimization
Yan Huang, Jinming Xu, Jiming Chen +1
This paper studies the distributed minimax optimization problem over networks. To enhance convergence performance, we propose a distributed optimistic gradient tracking method, ter…