Learning in mean-field games
The purpose of this paper is to show how insight obtained from a mean-field model can be
used to create an architecture for approximate dynamic programming (ADP) for a certain …
used to create an architecture for approximate dynamic programming (ADP) for a certain …
Joint admission control and routing via approximate dynamic programming for streaming video over software-defined networking
This paper considers the optimization problem of joint admission control and routing for the
video streaming service in wired software-defined networking (SDN). With the aid of the …
video streaming service in wired software-defined networking (SDN). With the aid of the …
Differential temporal difference learning
Value functions derived from Markov decision processes arise as a central component of
algorithms as well as performance metrics in many statistics and engineering applications of …
algorithms as well as performance metrics in many statistics and engineering applications of …
Learning techniques for feedback particle filter design
The feedback particle filter (FPF) is an approach to estimating the posterior distribution of the
states in a process-observation model. As in other versions of the particle filter, Monte Carlo …
states in a process-observation model. As in other versions of the particle filter, Monte Carlo …
Differential TD learning for value function approximation
Value functions arise as a component of algorithms as well as performance metrics in
statistics and engineering applications. Computation of the associated Bellman equations is …
statistics and engineering applications. Computation of the associated Bellman equations is …
Quasi-stochastic approximation and off-policy reinforcement learning
The Robbins-Monro stochastic approximation algorithm is a foundation of many algorithmic
frameworks for reinforcement learning (RL), and often an efficient approach to solving (or …
frameworks for reinforcement learning (RL), and often an efficient approach to solving (or …
Coordinating dispatch of distributed energy resources with model predictive control and Q-learning
Distributed energy resources such as renewable generators (wind, solar), energy storage,
and demand response can be used to complement fossil-fueled generators. The uncertainty …
and demand response can be used to complement fossil-fueled generators. The uncertainty …
Optimal rate of convergence for quasi-stochastic approximation
The Robbins-Monro stochastic approximation algorithm is a foundation of many algorithmic
frameworks for reinforcement learning (RL), and often an efficient approach to solving (or …
frameworks for reinforcement learning (RL), and often an efficient approach to solving (or …
The ieee computer society smart grid vision project opens opportunites for computational intelligence
The IEEE Computer Society Smart Grid Vision Project (CS-SGVP) was chartered to develop
Smart Grid visions looking forward as far as 30 years into the future. At the completion of the …
Smart Grid visions looking forward as far as 30 years into the future. At the completion of the …
[ספר][B] Reinforcement learning techniques for controlling resources in power networks
AS Kowli - 2013 - search.proquest.com
As power grids transition towards increased reliance on renewable generation, energy
storage and demand response resources, an effective control architecture is required to …
storage and demand response resources, an effective control architecture is required to …