Planning under uncertainty is a central problem in the study of automated sequential
decision making, and has been addressed by researchers in many different fields, including …

Learning, planning, and representing knowledge at multiple levels of temporal abstraction
are key, longstanding challenges for AI. In this paper we consider how these challenges can …

A critical issue for the application of Markov decision processes (MDPs) to realistic problems
is how the complexity of planning scales with the size of the MDP. In stochastic …

This paper addresses the problem of planning under uncertainty in large Markov Decision
Processes (MDPs). Factored MDPs represent a complex state space using state variables …

Markov decision processes (MDPs) have proven to be popular models for decision-theoretic
planning, but standard dynamic programming algorithms for solving MDPs rely on explicit …

Decision making usually involves choosing among different courses of action over a broad
range of time scales. For instance, a person planning a trip to a distant location makes high …

We study reinforcement learning (RL) in a setting with a network of agents whose states and
actions interact in a local manner where the objective is to find localized policies such that …

In a typical large-scale data center, a set of applications are hosted over virtual machines
(VMs) running on a large number of physical machines (PMs). Such a virtualization …

We present a provably efficient and near-optimal algorithm for reinforcement learning in
Markov decision processes (MDPs) whose transition model can be factored as a dynamic …

Partially observable Markov decision processes (POMDPs) provide a natural and principled
framework to model a wide range of sequential decision making problems under …