Index Catalog // ScholarsArchive@OSU

Learning-based control and coordination of autonomous UAVs

Creator:: Bell, Adam R.
Abstract:: Uninhabited aerial vehicles, also called UAVs are currently controller by a combination of a human pilot at a remote location, and autopilot systems similar to those found on commercial aircraft. As UAVs transition from remote piloting to fully autonomous operation, control laws must be developed for the tasks to be...
Resource Type:: Masters Thesis

Combining coordination mechanisms to improve performance in multi-robot teams

Creator:: Nasroullahi, Ehsan
Abstract:: Coordination is essential to achieving good performance in cooperative multiagent systems. To date, most work has focused on either implicit or explicit coordination mechanisms, while relatively little work has focused on the benefits of combining these two approaches. In this work we demonstrate that combining explicit and implicit mechanisms can...
Resource Type:: Masters Thesis

CLEAN learning to improve coordination and scalability in multiagent systems

Creator:: HolmesParker, Chris
Abstract:: Recent advances in multiagent learning have led to exciting new capabilities spanning fields as diverse as planetary exploration, air traffic control, military reconnaissance, and airport security. Such algorithms provide a tangible benefit over traditional control algorithms in that they allow fast responses, adapt to dynamic environments, and generally scale well....
Resource Type:: Dissertation
Full Text:: , Billy Bloudek, Adrian Agogino, and Orville Holmes. The support, advice, and help I have received from

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

D₊₊ : Structural Credit Assignment in Tightly Coupled Multiagent Domains

Creator:: Rahmattalabi, Aida
Abstract:: Autonomous multiagent teams can be used in complex exploration tasks to both expedite the exploration and improve the efficiency. However, use of multiagent systems presents additional challenges. Specifically, in domains where the agents' actions are tightly coupled, coordinating multiple agents to achieve cooperative behavior at the group level is difficult....
Resource Type:: Masters Thesis
Full Text:: would act to also increase the system evaluation function [36]. This work is similar to that of Agogino

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

Optimization Across Decomposition : A Multiagent Model of Collaboration in Decomposed System Design

Creator:: Hulse, Daniel E.
Abstract:: Complex engineered systems design is a collaborative activity. To design a system, experts from the relevant disciplines must work together to create the best overall system from their individual components. This situation is analogous to a multiagent system in which agents solve individual parts of a larger problem. Current multiagent...
Resource Type:: Masters Thesis

Learning in Complex Domains : Leveraging Multiple Rewards through Alignment

Creator:: Klinkhammer, Eric R.
Abstract:: In this thesis, we introduce alignment-based algorithms for improving the performance of reinforcement learning solutions for problems where the reward signal cannot be collapsed into a single number. Many real world problems require an agent to balance performance, longevity, and safety, and do so across different timelines. The key to...
Resource Type:: Masters Thesis
Full Text:: alignment is related to the concept of factoredness, as defined by Agogino and Tumer [1]. An agent’s reward

ScholarsArchive@OSU

Learning-based control and coordination of autonomous UAVs

Combining coordination mechanisms to improve performance in multi-robot teams

CLEAN learning to improve coordination and scalability in multiagent systems

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

Multiagent learning for locomotion and coordination in tensegrity robotics

Design and Analysis of Distributed Resource Allocation Techniques for Large-Scale Dynamic Spectrum Access Networks

D₊₊ : Structural Credit Assignment in Tightly Coupled Multiagent Domains

High-Dimensional Reinforcement Learning with Human Feedback

Optimization Across Decomposition : A Multiagent Model of Collaboration in Decomposed System Design

Learning in Complex Domains : Leveraging Multiple Rewards through Alignment

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