Edge AI: A taxonomy, systematic review and future directions
Abstract Edge Artificial Intelligence (AI) incorporates a network of interconnected systems
and devices that receive, cache, process, and analyse data in close communication with the …
and devices that receive, cache, process, and analyse data in close communication with the …
Policy optimization for continuous reinforcement learning
We study reinforcement learning (RL) in the setting of continuous time and space, for an
infinite horizon with a discounted objective and the underlying dynamics driven by a …
infinite horizon with a discounted objective and the underlying dynamics driven by a …
Hindsight learning for mdps with exogenous inputs
Many resource management problems require sequential decision-making under
uncertainty, where the only uncertainty affecting the decision outcomes are exogenous …
uncertainty, where the only uncertainty affecting the decision outcomes are exogenous …
Finite Sample Analysis of Average-Reward TD Learning and -Learning
The focus of this paper is on sample complexity guarantees of average-reward
reinforcement learning algorithms, which are known to be more challenging to study than …
reinforcement learning algorithms, which are known to be more challenging to study than …
Learning and information in stochastic networks and queues
We review the role of information and learning in the stability and optimization of queueing
systems. In recent years, techniques from supervised learning, online learning, and …
systems. In recent years, techniques from supervised learning, online learning, and …
Sample efficient reinforcement learning in mixed systems through augmented samples and its applications to queueing networks
This paper considers a class of reinforcement learning problems, which involve systems with
two types of states: stochastic and pseudo-stochastic. In such systems, stochastic states …
two types of states: stochastic and pseudo-stochastic. In such systems, stochastic states …
An online learning approach to dynamic pricing and capacity sizing in service systems
We study a dynamic pricing and capacity sizing problem in a GI/GI/1 queue, in which the
service provider's objective is to obtain the optimal service fee p and service capacity μ so as …
service provider's objective is to obtain the optimal service fee p and service capacity μ so as …
Online learning and pricing for service systems with reusable resources
We consider a price-based revenue management problem with finite reusable resources
over a finite time horizon T. Customers arrive following a price-dependent Poisson process …
over a finite time horizon T. Customers arrive following a price-dependent Poisson process …
Bayesian learning of optimal policies in markov decision processes with countably infinite state-space
S Adler, V Subramanian - Advances in Neural Information …, 2024 - proceedings.neurips.cc
Abstract Models of many real-life applications, such as queueing models of communication
networks or computing systems, have a countably infinite state-space. Algorithmic and …
networks or computing systems, have a countably infinite state-space. Algorithmic and …
Scalable deep reinforcement learning for ride-hailing
J Feng, M Gluzman, JG Dai - 2021 American Control …, 2021 - ieeexplore.ieee.org
Ride-hailing services, such as Didi Chuxing, Lyft, and Uber, arrange thousands of cars to
meet ride requests throughout the day. We consider a Markov decision process (MDP) …
meet ride requests throughout the day. We consider a Markov decision process (MDP) …