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Filtering by: Creator Billman, Eric J. Remove constraint Creator: Billman, Eric J. Advisor Fern, Alan Remove constraint Advisor: Fern, Alan Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad

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  • An Empirical Evaluation of Policy Rollout for Clue

    Creator:
    Marshall, Eric
    Abstract:
    We model the popular board game of Clue as an MDP and evaluate Monte-Carlo policy rollout in a simulated environment pitting different agents and policies against each other. We describe the choices we made in the representation, along with some of the problems we encountered along the way. We find...
    Resource Type:
    Capstone Project
    Full Text:
    An Empirical Evaluation of Policy Rollout for Clue® Eric Marshall Oregon State University M.S

  • Offensive Direction Inference in Real-World Football Video

    Creator:
    Lu, Qingkai
    Abstract:
    Automatic analysis of American football videos can help teams develop strategies and extract patterns with less human effort. In this work, we focus on the problem of automatically determining which team is on offense/defense, which is an important subproblem for higher-level analysis. While seemingly mundane, this problem is quite challenging...
    Resource Type:
    Masters Thesis

  • Investigating Latent State and Uncertainty Representations in Reinforcement Learning

    Creator:
    Koul, Anurag
    Abstract:
    Learning latent space representations of high-dimensional world states has been at the core of recent rapid growth in reinforcement learning(RL). At the same time, RL algo- rithms have suffered from ignored uncertainties in the predicted estimates of model-free or model-based methods. In our work, we investigate both of these aspects...
    Resource Type:
    Dissertation

  • Stake-Free Evaluations of Black-Box Optimization and Spatio-Temporal Graph Network Algorithms

    Creator:
    Merrill, Erich, III
    Abstract:
    Papers proposing novel machine learning algorithms tend to present the algorithm or technique in question in the best possible light. The standard practice is generally for authors to emphasize their proposed algorithms' performance in the precise setting where it is maximally impressive, often by only fully evaluating their best known...
    Resource Type:
    Dissertation
    Full Text:
    (n)) do 9: (h, i) =arg maxxh,j∈Lh g(xh,j), where Lh contains all leaves of τ at depth h 10: if g(xh