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Filtering by: Creator Duringer, J. M. Remove constraint Creator: Duringer, J. M. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad Subject Markov processes Remove constraint Subject: Markov processes

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  • 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:
    that maps each state to an action. The set of policies for an MDP M is denoted Π(M). An episode

  • Adiabatic Markov Decision Process : convergence of value iteration algorithm

    Creator:
    Duong, Thai
    Abstract:
    Markov Decision Process (MDP) is a well-known framework for devising the optimal decision making strategies under uncertainty. Typically, the decision maker assumes a stationary environment which is characterized by a time-invariant transition probability matrix. However, in many real-world scenarios, this assumption is not justified, thus the optimal strategy might not...
    Resource Type:
    Masters Thesis
    Full Text:
    ||vn0 − n0g ∗||∞ 1E ≤ + ∑ ∏ γk sp(vn0+1 − vn0 )n n i=n0 k=n0 i i−1 + Y M ei + 2 ∑ sp(v j)|ΔΦ j−1|(∏ γ

  • Data Collection in Sensor Networks via the Novel Fast Markov Decision Process Framework

    Creator:
    Duong, Thai
    Abstract:
    We investigate the data collection problem in sensor networks. The network consists of a number of stationary sensors deployed at different sites for sensing and storing data locally. A mobile element moves from sites to sites to collect data from the sensors periodically. There are different costs associated with the...
    Resource Type:
    Masters Thesis
    Full Text:
    function R. The transition probability matrix P specifies the dynamics of the system. Each entry P(i, j

  • Simulator-Defined MDP Planning with Applications in Natural Resource Management

    Creator:
    Alkaee Taleghan, Majid
    Abstract:
    This work is inspired by problems in natural resource management centered on the challenge of invasive species. Computing optimal management policies for maintaining ecosystem sustainable is challenging. Many ecosystem management problems can be formulated as MDP (Markov Decision Process) planning problems. In a simulator-defined MDP, the Markovian dynamics and rewards...
    Resource Type:
    Dissertation
    Full Text:
    negative reward to those states [26, 27]. This is equivalent to a so-called Big M method for establishing

  • 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:
    ); reward(2); for (i=1; im