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Filtering by: Creator Agogino, Adrian Remove constraint Creator: Agogino, Adrian Degree Field Computer Science Remove constraint Degree Field: Computer Science License All rights reserved Remove constraint License: All rights reserved

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Using RUBI to partition agents in air traffic problems with hard constraints and reward shaping

Creator:

Curran, William

Abstract:

Air traffic flow management over the U.S. airpsace is a difficult problem. Current management approaches lead to hundreds of thousands of hours of delay, costing billions of dollars annually. Weather and airport conditions may instigate this delay, but routing decisions balancing delay with congestion contribute significantly to the propagation of...

Resource Type:

Masters Thesis
Multiagent learning for locomotion and coordination in tensegrity robotics

Creator:

Iscen, Atil

Abstract:

Tensegrity structures are composed of pure compressional elements that are connected via a network of pure tensional elements. The concept of tensegrity promises numerous advantages to the field of robotics. Tensegrity robots are, however, notoriously difficult to control due to their oscillatory nature and nonlinear interaction between the components. Multiagent...

Resource Type:

Dissertation

Full Text:

NASA Ames Research Center. During and after my internship at Ames, Adrian Agogino and Vytas SunSpiral
Design and Analysis of Distributed Resource Allocation Techniques for Large-Scale Dynamic Spectrum Access Networks

Creator:

NoroozOliaee, MohammadJavad

Abstract:

The widespread use of wireless devices that we have recently been witnessing, such as smartphones, tablets, laptops, and wirelessly accessible devices in general, is causing an unprecedented growth in the required amount of the wireless radio spectrum. On the other hand, the spectrum resource has, for the last several decades,...

Resource Type:

Dissertation
High-Dimensional Reinforcement Learning with Human Feedback

Creator:

Curran, William

Abstract:

State-of-the-art personal robots need to perform complex manipulation tasks to be viable in complex scenarios. However, many of these robots, like the PR2, use manipulators with high degrees of freedom. High degrees of freedom are desirable from a functionality standpoint, but make the learning task more difficult by adding a...

Resource Type:

Dissertation

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Academic Affiliation

Electrical Engineering and Computer Science4
Mechanical, Industrial, and Manufacturing Engineering2

Advisor

Tumer, Kagan2
Hamdaoui, Bechir1
Hollinger, Geoffrey1
Smart, William D.1
Tadepalli, Prasad1

Commencement Year

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Current results range from 2013 to 2017

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Committee Member

Tadepalli, Prasad2
Bose, Bella1
Fern, Alan1
Hollinger, Geoffrey1
Palmer, Todd1
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Creator

Curran, William2
Iscen, Atil1
Noroozoliaee, Mohammadjavad1

Date

Date range begin – Date range end

Current results range from 2013 to 2017

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Decade

2010-20194

Degree Field

Computer Science[remove]4
Mechanical Engineering1
Robotics1

Degree Level

Doctoral3
Master's1

Degree Name

Doctor of Philosophy (Ph.D.)3
Master of Science (M.S.)1

Language

English [eng]4

License

All rights reserved[remove]4

Non-Academic Affiliation

Oregon State University. Graduate School4

Peer Reviewed

No4

Resource Type

Dissertation3
Masters Thesis1

Rights Statement

In Copyright4

Subject

Multiagent systems2
Reinforcement learning2
Air traffic control -- Computer programs1
Cognitive radio networks1
Evolutionary computation1
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