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Filtering by: Alternative title ssim No. 15 Remove constraint Alternative title ssim: No. 15 Advisor Fern, Alan P. Remove constraint Advisor: Fern, Alan P. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad

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  • Re-understanding Finite-State Representations of Recurrent Policy Networks

    Creator:
    Danesh, Mohamad H.
    Abstract:
    We propose an approach for understanding control policies represented as recurrent neural networks. Recent work has approached this problem by transforming such recurrent policy networks into finite-state machines (FSM) and then analyzing the equivalent minimized FSM. While this led to interesting insights, the minimization process can obscure a deeper understanding...
    Resource Type:
    Masters Thesis
    Full Text:
    they can provide. For example, while they are applied to recurrent policies, they provide no insight

  • Adversarial planning by strategy switching in a real-time strategy game

    Creator:
    King, Brian D.
    Abstract:
    We consider the problem of strategic adversarial planning in a Real-Time Strategy (RTS) game. Strategic adversarial planning is the generation of a network of high-level tasks to satisfy goals while anticipating an adversary's actions. In this thesis we describe an abstract state and action space used for planning in an...
    Resource Type:
    Masters Thesis
    Full Text:
    . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 4.4 Plan Grammar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 4.5 Example Plan

  • Monte Carlo Tree Search with Fixed and Adaptive Abstractions

    Creator:
    Hostetler, Jesse A.
    Abstract:
    Monte Carlo tree search (MCTS) is a class of online planning algorithms for Markov decision processes (MDPs) and related models that has found success in challenging applications. In the online planning approach, the agent makes a decision in the current state by performing a limited forward search over possible futures...
    Resource Type:
    Dissertation
    Full Text:
    functions µ 6= µ∗. Edge labels denote transition probabilities. . . . . . . . . . . . . . . . . . . . . . 15

  • Scheduling and Online Planning in Stochastic Diffusion Networks

    Creator:
    Xue, Shan
    Abstract:
    Diffusion processes in networks are common models for many domains, including species colonization, information/idea cascade, disease propagation and fire spreading. In diffusion networks, a diffusion event occurs when a behavior spreads from one node to the other following a probabilistic model, where the behavior could be species, an idea, a...
    Resource Type:
    Dissertation
    Full Text:
    offer advice and share valuable experience with me. Many of them are no longer at OSU now, but

  • Toward computer vision for understanding American football in video

    Creator:
    Hess, Robin W.
    Abstract:
    In this work, I examine the problem of understanding American football in video. In particular, I present several mid-level computer vision algorithms that each accomplish a different sub-task within a larger system for annotating, interpreting, and analyzing collections of American football video. The analysis of football video is useful in...
    Resource Type:
    Dissertation
    Full Text:
    in no small part to your skill as an advisor. I am also extremely grateful to the other OSU Computer

  • Incorporating and Learning Behavior Constraints for Sequential Decision Making

    Creator:
    Pinto, Jervis
    Abstract:
    Writing a program that performs well in a complex environment is a challenging task. In such problems, a method of deterministic programming combined with reinforcement learning (RL) can be helpful. However, current systems either force developers to encode knowledge in very specific forms (e.g., state-action features), or assume advanced RL...
    Resource Type:
    Dissertation
    Full Text:
    ) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.5.2 Deficiencies of SARSA(λ) . . . . . . . . . . . . . . . . . . . . . . . . 15 2.5.3 iSARSA(λ

  • Learning and Improving Policies for Probabilistic Planning Problems

    Creator:
    Issakkimuthu, Murugeswari
    Abstract:
    In this work, we study the problem of learning and improving policies for probabilistic planning problems. In the first part, we train neural network policies for probabilistic planning problems modeled as factored Markov decision problems. The objective is to train problem-specific neural networks via supervised learning to imitate the action...
    Resource Type:
    Dissertation
    Full Text:
    Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.3 Wildfire Results

  • New learning modes for sequential decision making

    Creator:
    Judah, Kshitij
    Abstract:
    This thesis considers the problem in which a teacher is interested in teaching action policies to computer agents for sequential decision making. The vast majority of policy learning algorithms o er teachers little flexibility in how policies are taught. In particular, one of two learning modes is typically considered: 1)...
    Resource Type:
    Dissertation
    Full Text:
    learning policies with regret no more than � with probability at least 1− δ. Np(�′, δ′) and Na(�′, δ′, D