Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 3 of 3
Full-Text Articles in Entire DC Network
Improving Asynchronous Advantage Actor Critic With A More Intelligent Exploration Strategy, James B. Holliday
Improving Asynchronous Advantage Actor Critic With A More Intelligent Exploration Strategy, James B. Holliday
Graduate Theses and Dissertations
We propose a simple and efficient modification to the Asynchronous Advantage Actor Critic (A3C)
algorithm that improves training. In 2016 Google’s DeepMind set a new standard for state-of-theart
reinforcement learning performance with the introduction of the A3C algorithm. The goal of
this research is to show that A3C can be improved by the use of a new novel exploration strategy we
call “Follow then Forage Exploration” (FFE). FFE forces the agents to follow the best known path
at the beginning of a training episode and then later in the episode the agent is forced to “forage”
and explores randomly. In …
Parameterizing And Aggregating Activation Functions In Deep Neural Networks, Luke Benjamin Godfrey
Parameterizing And Aggregating Activation Functions In Deep Neural Networks, Luke Benjamin Godfrey
Graduate Theses and Dissertations
The nonlinear activation functions applied by each neuron in a neural network are essential for making neural networks powerful representational models. If these are omitted, even deep neural networks reduce to simple linear regression due to the fact that a linear combination of linear combinations is still a linear combination. In much of the existing literature on neural networks, just one or two activation functions are selected for the entire network, even though the use of heterogenous activation functions has been shown to produce superior results in some cases. Even less often employed are activation functions that can adapt their …
Collaborative Robotic Path Planning For Industrial Spraying Operations On Complex Geometries, Steven Brown
Collaborative Robotic Path Planning For Industrial Spraying Operations On Complex Geometries, Steven Brown
Graduate Theses and Dissertations
Implementation of automated robotic solutions for complex tasks currently faces a few major hurdles. For instance, lack of effective sensing and task variability – especially in high-mix/low-volume processes – creates too much uncertainty to reliably hard-code a robotic work cell. Current collaborative frameworks generally focus on integrating the sensing required for a physically collaborative implementation. While this paradigm has proven effective for mitigating uncertainty by mixing human cognitive function and fine motor skills with robotic strength and repeatability, there are many instances where physical interaction is impractical but human reasoning and task knowledge is still needed. The proposed framework consists …