As the distributed-memory parallel systems become heterogeneous in nature, it is important to devise scheduling policies that take node heterogeneity into account. Previous studies on this topic have focused on homogeneous systems. In this paper, we propose a new two-level space-sharing policy for heterogeneous systems. The proposed policy is an adaptive space-sharing policy that takes system l...

Temporal-difference-based deep-reinforcement learning methods have typically been driven by off-policy, bootstrap Q-Learning updates. In this paper, we investigate the effects of using on-policy, Monte Carlo updates. Our empirical results show that for the DDPG algorithm in a continuous action space, mixing on-policy and off-policy update targets exhibits superior performance and stability comp...

In this paper we examine a collective decision problem, where the set of heterogeneous individuals is partitioned into several groups, each choosing its own policy (e.g., location of a public project) from the given policy space. The model is that of “horizontal product differentiation” where individuals display distinct preferences over the policy space. We first consider the notion of “effici...

With the digital TV transition, TV white space issue stands out. While economic value of TV white space is estimated to be about 200~320 million pounds, there are several problems such as spectrum interference, publicity and business model issues. To analyze current spectrum management policy in utilizing TV white space in Korea, this research conducted FGI based on PEST frameworks. As a result...