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Filtering by: Creator Carey, Andrew G. Remove constraint Creator: Carey, Andrew G. Advisor Tumer, Kagan Remove constraint Advisor: Tumer, Kagan License All rights reserved Remove constraint License: All rights reserved

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  • Learning-based Control of Experimental Hybrid Fuel Cell Power Plant

    Creator:
    Gabler, Andrew
    Abstract:
    Direct red Solid Oxide Fuel Cell (SOFC) Turbine hybrid plants have the potential to dramatically increase power plant efficiency, decrease emissions, and provide fast response to transient loads. The US Department of Energy's (DOE) Hybrid Performance Project is an experimental hybrid SOFC plant, built at the National Energy Technology Laboratory...
    Resource Type:
    Masters Thesis
    Full Text:
    AN ABSTRACT OF THE THESIS OF Andrew Gabler for the degree of

  • 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:
    observations to be made to account as success. . . . 25 4.2 Observation performance of policies trained on G

  • Multi-Reward Learning and Sparse Rewards

    Creator:
    Christopher, Reid M.
    Abstract:
    Reinforcement learning has made impressive strides in solving problems in challenging domains, but problems are increasingly being described with sparse rewards. Sparse rewards directly reduce the rate at which useful feedback is provided to the learner and make it difficult to distinguish between what specific actions led to the reception...
    Resource Type:
    Capstone Project

  • Multiagent Learning via Dynamic Skill Selection

    Creator:
    Sachdeva, Enna
    Abstract:
    Multiagent coordination has many real-world applications such as self-driving cars, inventory management, search and rescue, package delivery, traffic management, warehouse management, and transportation. These tasks are generally character-ized by a global team objective that is often temporally sparse - realized only upon completing an episode. The sparsity of the shared...
    Resource Type:
    Masters Thesis
    Full Text:
    the team’s performance [16]. Di(z) = G(z)−G(zi ∪ ci) where z is the joint state, G(z) is the global

  • Coevolution and transfer learning in a point-to-point fleet coordination problem

    Creator:
    Yliniemi, Logan Michael
    Abstract:
    In this work we present a multiagent Fleet Coordination Problem (FCP). In this formulation, agents seek to minimize the fuel consumed to complete all deliveries while maintaining acceptable on-time delivery performance. Individual vehicles must both (i) bid on the rights to deliver a load of goods from origin to destination...
    Resource Type:
    Masters Thesis
    Full Text:
    to thank Carey E. and Elizabeth P. Yliniemi for their support and guidance through the years

  • Counterfactual thinking applied to multiagent systems

    Creator:
    Zerbel, Nicholas
    Abstract:
    Multiagent approaches are well suited to designing autonomous solutions for systems that feature complex interactions between many individuals such as in autonomous traffic systems and multi-robot exploration systems. However, creating autonomous agents that function effectively in these systems is a challenging task. In these complex environments, agents need informative reward...
    Resource Type:
    Dissertation
    Full Text:
    from training conditions, G, D, and D++ performance degrades rapidly. Using CKI, agents are provided

  • Providing Informative Feedback for Learning in Tightly Coupled Multiagent Domains

    Creator:
    Rockefeller, Golden C.
    Abstract:
    Autonomous agents that sense, decide, act, and coordinate effectively with each other are critical in many real-world domains such as autonomous driving, search and rescue missions, air traffic management, and underwater or deep space exploration. All such domains share a key difficulty: though high-level mission goals are clear to system...
    Resource Type:
    Dissertation

  • Multi-Objective Optimization in Multiagent Systems

    Creator:
    Yliniemi, Logan Michael
    Abstract:
    Cooperative multiagent systems are used as solution concepts in many application domains including air traffic control, satellite communications, and extra planetary exploration. As systems become more distributed and complex, we observe three phenomena. First, these systems cannot be accurately modeled, rendering traditional model based control methods inadequate. Second, system parameters...
    Resource Type:
    Dissertation
    Full Text:
    path. Finally, I would like to thank Carey E. and Elizabeth P. Yliniemi for their support and

  • 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:
    gradient of G strongly match. . . . . . . . 51 LIST OF FIGURES (Continued) Figure Page 6.6 The

  • 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
    Full Text:
    over time for 3 agents where agents are rewarded using G, D, L, or a random policy is used. We see that