增强主体:数字审美经验中的主体形态

在数字审美活动中,主体既不是传统的古典主义个体,也有别于后现代视野下的碎片主体,而是一种在数字交互技术和智能移动终端设备加持下的“增强主体”。由于数字审美界面已经从静观的对象,转变为主体进入其中并自我现象化的空间,这种增强主体首先是一种人机耦合后,以运动感为核心的扩容主体。其次,这种增强主体还意味着“皮肤自我”的深度复苏,带有全能色彩的“皮肤自我”将审美主体提升到神祇化高度。最后,这种增强主体还是一种与他人共在的联通主体,这使得审美感受在厚度与强度上都将远超传统的静观审美。从本质上讲,这个协调着身体与机器、现实与虚拟、自我与他人、意识与无意识、视觉与触感的增强主体,是后现代之后的一种崭新主体。

党员主体意识探析

党员意识淡化,先进模范作用不明显,党员难以适应形势的要求和肩负艰巨、繁重、复杂的历史使命。党员意识主要表现为党员的主体意识,能否增强党员主体意识是党员能否发挥先进模范作用的先决思想条件,当前党员主体意识薄弱有种种表现,说明增强党员主体意识势在必行。增强党员主体意识要把好＂四关＂：入口关、考核关、奖励关和惩治关。同时要着力于提升党员的学习意识、忧患意识、组织意识、责任意识等。

Multi-agent reinforcement learning with cooperation based on eligibility traces

The application of reinforcement learning is widely used by multi-agent systems in recent years. An agent uses a multi-agent system to cooperate with other agents to accomplish the given task, and one agent′s behavior usually affects the others′ behaviors. In traditional reinforcement learning, one agent takes the others location, so it is difficult to consider the others′ behavior, which decreases the learning efficiency. This paper proposes multi-agent reinforcement learning with cooperation based on eligibility traces, i.e. one agent estimates the other agent′s behavior with the other agent′s eligibility traces. The results of this simulation prove the validity of the proposed learning method.