Reflect

增强学习 经典算法 A3C 论文摘要： We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers. We present asynchronous variants of four standard reinforcement learning algorithms and show that parallel actor-learners have a stabilizing effect on training allowing all four methods to successfully train neural network controllers. The best performing method, an asynchronous variant of actor-critic, surpasses the current state-of-the-art on the Atari domain while training for half the time on a single multi-core CPU instead of a GPU. Furthermore, we show that asynchronous actor-critic succeeds on a wide variety of continuous motor control problems as well as on a new task of navigating random 3D mazes using a visual input.

描述kmp算法的原始论文，内容十分详尽，很有价值

Image Segmentation via Adaptive -Mean Clustering and Knowledge-Based Morphological Operations with Biomedical Applications by Chang Wen Chen, Senior Member, IEEE, Jiebo Luo, Member, IEEE,and Kevin J. Parker, Fellow, IEEE