6,027 research outputs found
Statistical-mechanics approach to a reinforcement learning model with memory
We introduce a two-player model of reinforcement learning with memory. Past
actions of an iterated game are stored in a memory and used to determine
player's next action. To examine the behaviour of the model some approximate
methods are used and confronted against numerical simulations and exact master
equation. When the length of memory of players increases to infinity the model
undergoes an absorbing-state phase transition. Performance of examined
strategies is checked in the prisoner' dilemma game. It turns out that it is
advantageous to have a large memory in symmetric games, but it is better to
have a short memory in asymmetric ones.Comment: 6 pages, some additional numerical calculation
Adaptive energy minimization of OpenMP parallel applications on many-core systems
Energy minimization of parallel applications is an emerging challenge for current and future generations of many-core computing systems. In this paper, we propose a novel and scalable energy minimization approach that suitably applies DVFS in the sequential part and jointly considers DVFS and dynamic core allocations in the parallel part. Fundamental to this approach is an iterative learning based control algorithm that adapt the voltage/frequency scaling and core allocations dynamically based on workload predictions and is guided by the CPU performance counters at regular intervals. The adaptation is facilitated through performance annotations in the application codes, defined in a modified OpenMP runtime library. The proposed approach is validated on an Intel Xeon E5-2630 platform with up to 24 CPUs running NAS parallel benchmark applications. We show that our proposed approach can effectively adapt to different architecture and core allocations and minimize energy consumption by up to 17% compared to the existing approaches for a given performance requirement
- …