Reinforcement learning for continuous control

Reinforcement learning is an area in machine learning that concerns how agents should take actions in an environment to maximize its reward. Earlier works on reinforcement learning algorithms work well for discrete action and discrete observations. In the continuous environment the actions are infinite in number and observations are also infinite. So certain reinforcement algorithms were developed in order to learn on continuous spaces. Among those, two algorithms are Stochastic Synapse Reinforcement Learning (SSRL) and Deep Deterministic Policy Gradient (DDPG). Previous works used these algorithms on different environments separately in order to understand the behaviour of the algorithms. In this thesis, a comparison study between between SSRL and DDPG is made by using them on the same continuous environments and observing how each algorithm behaves on each environment and what kind of strengths and weaknesses can be inferred by comparing the algorithms. The algorithms are made to run on two continuous environments, namely mountain car continuous and pendulum. They are run 10 times for each set of time steps like 2000, 3000, 4000 for 1000 episodes each and the cumulative reward at the end of each episode is found. The episode is the length of the simulation at end of which the algorithm ends in a terminal state. The average and standard deviation of cumulative rewards across 10 repetitions for each time step and all the repetitions across different time steps are also collected. The results shows different trends across different experiments. Based on the results it can be inferred that overall SSRL performs consistently even though it does not gains rewards like DDPG whereas DDPG performs inconsistently but certain rewards it earns are higher than those of SSRL. Also in the case of the delayed-reinforcement pendulum environment both algorithms do not learn well, showing their weakness towards environments whose terminal state is not definite

Research on Brain and Mind Inspired Intelligence

To address the problems of scientific theory, common technology and engineering application of multimedia and multimodal information computing, this paper is focused on the theoretical model, algorithm framework, and system architecture of brain and mind inspired intelligence (BMI) based on the structure mechanism simulation of the nervous system, the function architecture emulation of the cognitive system and the complex behavior imitation of the natural system. Based on information theory, system theory, cybernetics and bionics, we define related concept and hypothesis of brain and mind inspired computing (BMC) and design a model and framework for frontier BMI theory. Research shows that BMC can effectively improve the performance of semantic processing of multimedia and cross-modal information, such as target detection, classification and recognition. Based on the brain mechanism and mind architecture, a semantic-oriented multimedia neural, cognitive computing model is designed for multimedia semantic computing. Then a hierarchical cross-modal cognitive neural computing framework is proposed for cross-modal information processing. Furthermore, a cross-modal neural, cognitive computing architecture is presented for remote sensing intelligent information extraction platform and unmanned autonomous system

NASA Space Engineering Research Center Symposium on VLSI Design

The NASA Space Engineering Research Center (SERC) is proud to offer, at its second symposium on VLSI design, presentations by an outstanding set of individuals from national laboratories and the electronics industry. These featured speakers share insights into next generation advances that will serve as a basis for future VLSI design. Questions of reliability in the space environment along with new directions in CAD and design are addressed by the featured speakers

Technology 2000, volume 1

The purpose of the conference was to increase awareness of existing NASA developed technologies that are available for immediate use in the development of new products and processes, and to lay the groundwork for the effective utilization of emerging technologies. There were sessions on the following: Computer technology and software engineering; Human factors engineering and life sciences; Information and data management; Material sciences; Manufacturing and fabrication technology; Power, energy, and control systems; Robotics; Sensors and measurement technology; Artificial intelligence; Environmental technology; Optics and communications; and Superconductivity

2009 Annual Progress Report: DOE Hydrogen Program

This report summarizes the hydrogen and fuel cell R&D activities and accomplishments of the DOE Hydrogen Program for FY2009. It covers the program areas of hydrogen production and delivery; fuel cells; manufacturing; technology validation; safety, codes and standards; education; and systems analysis