Approximate dynamic programming using fluid and diffusion approximations with applications to power management

Abstract

Neuro-dynamic programming is a class of powerful techniques for approximating the solution to dynamic programming equations. In their most computationally attractive formulations, these techniques provide the approximate solution only within a prescribed finite-dimensional function class. Thus, the question that always arises is how should the function class be chosen? The goal of this paper is to propose an approach using the solutions to associated fluid and diffusion approximations. In order to illustrate this approach, the paper focuses on an application to dynamic speed scaling for power management in computer processors.

Submitted to SIAM Journal on Control and Optimization (SICON), July 2013

References in corpus (1)

• Approximate dynamic programming using fluid and diffusion approximations with applications to power management

Cited by in corpus (5)

• Queueing Network Controls via Deep Reinforcement Learning
• Approximate dynamic programming using fluid and diffusion approximations with applications to power management
• On the tradeoff of average delay, average service cost, and average utility for single server queues with monotone policies
• Distributed Stochastic Optimization for Weakly Coupled Systems with Applications to Wireless Communications
• Evolutionary game of coalition building under external pressure