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Filtering by: Creator Bakalinsky, Alan T. Remove constraint Creator: Bakalinsky, Alan T. Advisor Fern, Alan Remove constraint Advisor: Fern, Alan

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  • Robust Reference-Free Sim-to-Real Reinforcement Learning for Bipedal Locomotion

    Creator:
    Siekmann, Jonah A.
    Abstract:
    In recent years, model-free Deep Reinforcement Learning (RL) has become an increasingly popular alternative to more traditional model-based or optimization-based control methods in solving robotic legged locomotion. However, deploying RL in the real world can be a significant undertaking. Constructing reward functions which compel controllers to learn the desired behavior...
    Resource Type:
    Masters Thesis
    Full Text:
    -Free Sim-to-Real Reinforcement Learning for Bipedal Locomotion Abstract approved: Alan Fern In

  • Revisiting output coding for sequential supervised learning

    Creator:
    Hao, Guohua
    Abstract:
    Markov models are commonly used for joint inference of label sequences. Unfortunately, inference scales quadratically in the number of labels, which is problematic for training methods where inference is repeatedly preformed and is the primary computational bottleneck for large label sets. Recent work has used output coding to address this...
    Resource Type:
    Masters Thesis
    Full Text:
    Sequential Supervised Learning Abstract approved: Alan Fern Markov models are commonly used for joint

  • Object Tracking-by-Segmentation in Videos

    Creator:
    Chen, Sheng
    Abstract:
    This thesis focuses on the problem of object tracking. Given a video, the general objective of tracking is to track the location over time of one or more targets in the image sequence. This is a very challenging task as algorithms need to deal with problems such as appearance variations,...
    Resource Type:
    Dissertation
    Full Text:
    -by-Segmentation in Videos Abstract approved: Alan P. Fern This thesis focuses on the problem of

  • A SAT-based planning framework for optimizing resource production

    Creator:
    Ventura, Christopher
    Abstract:
    Domain-independent automated planning is concerned with computing a sequence of actions that can transform an initial state into a desired goal state. Resource production domains form an interesting class of such problems, in that they typically require reasoning about concurrent durative-actions with continuous effects while minimizing some cost function. Although...
    Resource Type:
    Masters Thesis
    Full Text:
    Production Abstract approved: Alan Fern Domain-independent automated planning is concerned with computing

  • Learning from optimization for bipedal robots

    Creator:
    Yu, Fangzhou
    Abstract:
    Legged robots have consistently captured our collective imagination through various forms of media, from Hollywood films, anime, and viral Youtube videos of robots accomplishing incredible feats of acrobatics. These robots have the potential to navigate our environments, capable of completing tasks that would otherwise require human intervention. However, developing controls...
    Resource Type:
    Masters Thesis
    Full Text:
    Optimization For Bipedal Robots Abstract approved: Alan Fern ©Copyright by Fangzhou Yu August 30, 2023

  • Reinforcement Learning for P2P Backup Applications

    Creator:
    Mall, Shikhar
    Abstract:
    A five year study of file-system metadata shows that the number of files increases by 200% and only a select few file-types contribute for over 35% of the files that exist on a file-system. It is difficult to point out a permanent selection of files that a user really cares...
    Resource Type:
    Capstone Project
    Full Text:
    Where, T is the temperature. Larger T leads to greater exploitation. We start with value of 100 for T

  • Anomaly Detection: Theory, Explanation and User Feedback

    Creator:
    Siddiqui, Md Amran
    Abstract:
    Anomaly detection has been used in variety of applications in practice, including cyber-security, fraud detection and detecting faults in safety critical systems, etc. Anomaly detectors produce a ranked list of statistical anomalies, which are typically examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, most...
    Resource Type:
    Dissertation
    Full Text:
    : Theory, Explanation and User Feedback Abstract approved: Alan P. Fern Anomaly detection has been used

  • 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:
    Abstract approved: Alan Fern Papers proposing novel machine learning algorithms tend to present the

  • UCT for tactical assault battles in real-time strategy games

    Creator:
    Balla, Radha-Krishna
    Abstract:
    We consider the problem of tactical assault planning in real-time strategy games where a team of friendly agents must launch an assault on an enemy. This problem offers many challenges including a highly dynamic and uncertain environment, multiple agents, durative actions, numeric attributes, and different optimization objectives. While the dynamics...
    Resource Type:
    Masters Thesis
    Full Text:
    Alan Fern We consider the problem of tactical assault planning in real-time strategy games

  • Learning ranking functions for efficient search

    Creator:
    Xu, Yuehua
    Abstract:
    This dissertation explores algorithms for learning ranking functions to efficiently solve search problems, with application to automated planning. Specifically, we consider the frameworks of beam search, greedy search, and randomized search, which all aim to maintain tractability at the cost of not guaranteeing completeness nor optimality. Our learning objective for...
    Resource Type:
    Dissertation
    Full Text:
    : Alan Fern This dissertation explores algorithms for learning ranking functions to efficiently solve