Highlights from RHIC Spin Physics Program

The Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Laboratory delivers the world's highest energy polarized proton-proton collisions at a center of mass energy up to 500 GeV and provides a unique opportunity to study the quark and gluon spin structure of the proton and QCD dynamics at high energy scale. RHIC has produced many exiting physics results in recent years. The latest data from RHIC significantly constrain the gluon spin contribution to the proton spin, and the parity violating single spin asymmetry are observed for the first time in W production by both the PHENIX and STAR collaborations. In this report, I present the latest results from the PHENIX and STAR experiments, followed by a brief discussion of the future prospects of transverse physics, particularly on the importance of the unique measurements of Drell-Yan single spin asymmetry.Comment: 4 pages, MENU2010 proceeding

Lifelong Federated Reinforcement Learning: A Learning Architecture for Navigation in Cloud Robotic Systems

This paper was motivated by the problem of how to make robots fuse and transfer their experience so that they can effectively use prior knowledge and quickly adapt to new environments. To address the problem, we present a learning architecture for navigation in cloud robotic systems: Lifelong Federated Reinforcement Learning (LFRL). In the work, We propose a knowledge fusion algorithm for upgrading a shared model deployed on the cloud. Then, effective transfer learning methods in LFRL are introduced. LFRL is consistent with human cognitive science and fits well in cloud robotic systems. Experiments show that LFRL greatly improves the efficiency of reinforcement learning for robot navigation. The cloud robotic system deployment also shows that LFRL is capable of fusing prior knowledge. In addition, we release a cloud robotic navigation-learning website based on LFRL