Search Constraints

Start Over
Filtering by: Creator Agogino, Adrian Remove constraint Creator: Agogino, Adrian Committee Member Hollinger, Geoff Remove constraint Committee Member: Hollinger, Geoff Decade 2010-2019 Remove constraint Decade: 2010-2019

Search Results

  • 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

  • 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