[PDF][PDF] Federated natural policy gradient methods for multi-task reinforcement learning
Federated reinforcement learning (RL) enables collaborative decision making of multiple
distributed agents without sharing local data trajectories. In this work, we consider a multi …
distributed agents without sharing local data trajectories. In this work, we consider a multi …
Global Convergence of Natural Policy Gradient with Hessian-Aided Momentum Variance Reduction
Natural policy gradient (NPG) and its variants are widely-used policy search methods in
reinforcement learning. Inspired by prior work, a new NPG variant coined NPG-HM is …
reinforcement learning. Inspired by prior work, a new NPG variant coined NPG-HM is …
Natural Policy Gradient and Actor Critic Methods for Constrained Multi-Task Reinforcement Learning
Multi-task reinforcement learning (RL) aims to find a single policy that effectively solves
multiple tasks at the same time. This paper presents a constrained formulation for multi-task …
multiple tasks at the same time. This paper presents a constrained formulation for multi-task …
A Zeroth-Order Variance-Reduced Method for Decentralized Stochastic Non-convex Optimization
In this paper, we consider a distributed stochastic non-convex optimization problem, which is
about minimizing a sum of $ n $ local cost functions over a network with only zeroth-order …
about minimizing a sum of $ n $ local cost functions over a network with only zeroth-order …