Human motion trajectory prediction: A survey
With growing numbers of intelligent autonomous systems in human environments, the ability
of such systems to perceive, understand, and anticipate human behavior becomes …
of such systems to perceive, understand, and anticipate human behavior becomes …
Reinforcement learning for demand response: A review of algorithms and modeling techniques
JR Vázquez-Canteli, Z Nagy - Applied energy, 2019 - Elsevier
Buildings account for about 40% of the global energy consumption. Renewable energy
resources are one possibility to mitigate the dependence of residential buildings on the …
resources are one possibility to mitigate the dependence of residential buildings on the …
Minimum cost flows, MDPs, and ℓ1-regression in nearly linear time for dense instances
In this paper we provide new randomized algorithms with improved runtimes for solving
linear programs with two-sided constraints. In the special case of the minimum cost flow …
linear programs with two-sided constraints. In the special case of the minimum cost flow …
An approach for service function chain routing and virtual function network instance migration in network function virtualization architectures
V Eramo, E Miucci, M Ammar… - IEEE/ACM Transactions …, 2017 - ieeexplore.ieee.org
Network function virtualization foresees the virtualization of service functions and their
execution on virtual machines. Any service is represented by a service function chain (SFC) …
execution on virtual machines. Any service is represented by a service function chain (SFC) …
Near-optimal time and sample complexities for solving Markov decision processes with a generative model
In this paper we consider the problem of computing an $\epsilon $-optimal policy of a
discounted Markov Decision Process (DMDP) provided we can only access its transition …
discounted Markov Decision Process (DMDP) provided we can only access its transition …
Joint status sampling and updating for minimizing age of information in the Internet of Things
The effective operation of time-critical Internet of things (IoT) applications requires real-time
reporting of fresh status information of underlying physical processes. In this paper, a real …
reporting of fresh status information of underlying physical processes. In this paper, a real …
On the convergence of projective-simulation–based reinforcement learning in Markov decision processes
WL Boyajian, J Clausen, LM Trenkwalder… - Quantum machine …, 2020 - Springer
In recent years, the interest in leveraging quantum effects for enhancing machine learning
tasks has significantly increased. Many algorithms speeding up supervised and …
tasks has significantly increased. Many algorithms speeding up supervised and …
Offloading time optimization via Markov decision process in mobile-edge computing
Computation offloading from a mobile device to the edge server is an emerging paradigm to
reduce completion latency of intensive computations in mobile-edge computing (MEC). In …
reduce completion latency of intensive computations in mobile-edge computing (MEC). In …
[图书][B] Reinforcement learning: An introduction
The significantly expanded and updated new edition of a widely used text on reinforcement
learning, one of the most active research areas in artificial intelligence. Reinforcement …
learning, one of the most active research areas in artificial intelligence. Reinforcement …
Reinforcement learning: A survey
This paper surveys the field of reinforcement learning from a computer-science perspective.
It is written to be accessible to researchers familiar with machine learning. Both the historical …
It is written to be accessible to researchers familiar with machine learning. Both the historical …