Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 47 for “"MDPs"”.
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Generic Reinforcement Learning Beyond Small MDPs
… been used to represent deterministic POMDPs. We show the best existing results on the TMaze domain and good results on larger domains that require long-term memory. We introduce a new value-based cost function that can be evaluated model-free. The value- based cost allows for smaller …
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Generic Reinforcement Learning Beyond Small MDPs
… been used to represent deterministic POMDPs. We show the best existing results on the TMaze domain and good results on larger domains that require long-term memory. We introduce a new value-based cost function that can be evaluated model-free. The value- based cost allows for smaller …
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Fast approximate hierarchical solution of MDPs
… models of large Markov decision processes (MDPs). As the size of the MDP increases, finding an exact solution becomes intractable, so we expect only to find an approximate solution. We also assume that the hierarchies we create are not necessarily applicable to more than one problem so that …
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Solving Large MDPs Quickly with Partitioned Value Iteration
… a viable algorithm for solving large-scale MDPs because it converges too slowly. However, its performance can be dramatically improved by eliminating redundant or useless backups, and by backing up states in the right order. We present several methods designed to help structure value …
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Solving Dec-MDPs with options and intention recognition
… we instead solve a set of single-agent MDPs, that we can solve in P-Complete, and combine these solutions during execution time. We tested our algorithm on several instances of the Bribed Package Retrieval Problem and we were able to handle problems as large as our MDP solver would …
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Near-Optimal Learning and Planning in Separated Latent MDPs
… of learning Latent Markov Decision Processes (LMDPs). In this model, the learner interacts with an MDP drawn at the beginning of each epoch from an unknown mixture of MDPs. To sidestep known impossibility results, we consider several notions of δ-separation of the constituent MDPs. The main …
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Robust, risk-sensitive, and data-driven control of Markov Decision Processes
Markov Decision Processes (MDPs) model problems of sequential decision-making under uncertainty. They have been studied and applied extensively. Nonetheless, there are two major barriers that still hinder the applicability of MDPs to many more practical decision making problems: * The decision …
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An Invisible Issue of Task Underspecification in Deep Reinforcement Learning Evaluations
… selected point Markov decision processes (point MDPs), stemming from task underspecification. A large class of DRL tasks, particularly in real-world decision problems, induce a family of MDPs, which---perhaps confusingly---each has the same high-level problem definition. As a demonstrative …
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The Power of Municipal Development Plans: An Examination of Their Relevance and Impact in Guatemala
… and participated in the formulation of various MDPs. This experience provided the foundation for this research. I interviewed representatives of the three organizations most actively involved in the formulation of MDPs and a number of local participants including mayors, community leaders, …
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Local multiagent control in large factored planning Problems
… multiagent Markov Decision Processes (MDPs). To achieve this, the proposed approximation architectures assume that the solution of the overall system can be represented with sparsely interacting (i.e., local) value function components that -- if found -- approximate the global solution …
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Kernel-based approximate dynamic programming using Bellman residual elimination
… be naturally posed as Markov Decision Processes (MDPs). An important advantage of the MDP framework is the ability to utilize stochastic system models, thereby allowing the system to make sound decisions even if there is randomness in the system evolution over time. Unfortunately, the curse of …
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Uniform positive recurrence and long term behavior of Markov decision processes, with applications in sensor scheduling
… for discrete-time Markov decision processes (MDPs). First, we adapt two recent results in controlled diffusion processes to suit countable state MDPs by making assumptions that approximate continuous behavior. We show that if the MDP is stable under any stationary policy, then it must be …
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The impact of gender differences in social networks on microenterprise performance
… the owner. Microenterprise development programs (MDPs), which provide capital, business training, technical support, and access to social networks, were introduced to the United States as an alternative strategy for providing low-income women with economic opportunities. One of the important …
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Verification of linear-time properties for finite probabilistic systems
… this question for Markov Decision Processes (MDPs), which are finite state models involving stochastic and non-deterministic behaviour over discrete time steps. The kind of specifications we focus on are those that describe the correctness of individual executions of the model, called linear …
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Tensor decomposition and parallelization of Markov Decision Processes
Markov Decision Processes (MDPs) with large state spaces arise frequently when applied to real world problems. Optimal solutions to such problems exist, but may not be computationally tractable, as the required processing scales exponentially with the number of states. Unsurprisingly, investigating …
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Acceleration of Iterative Methods for Markov Decision Processes
… methods for classes of the expected discounted MDPs and average cost MDPs. We establish a class of operators that can be integrated into value iteration and modified policy iteration algorithms for Markov Decision Processes, so as to speed up the convergence of the iterative search. Application …
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Stochastic Dynamic Optimization and Games in Operations Management
… management.</p><p>Markov decision processes (MDPs) and sequential games are good models of many real sequential decision processes. However, in diverse applications in operations research and economics, the state of the MDP is a vector and the curse of dimensionality obstructs analysis and …
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Statistical Methods for Off-Policy Learning
… horizons, and Markov decision processes (MDPs) in reinforcement learning (RL), which focus on dimension reduction in closed systems such as games. Many real-world problems bear resemblance to both MDPs and DTRs. Yet, the absence of a general methodology compels practitioners to choose one …
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A sensitivity analysis of cyber contingency ranking within the SOCCA framework
… heavily on Markov Decision Processes. These MDPs require expert knowledge in determining the attack surface and gauging the likelihood of an attack’s success as represented by a probability. The choice of reward function and assignment of probabilities greatly influence the behavior of the …
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Bayesian Theory of Mind : modeling human reasoning about beliefs, desires, goals, and social relations
… planning, such as Markov decision processes (MDPs), partially observable MDPs (POMDPs), and Markov games. ToM reasoning will be formalized as rational probabilistic inference over these models of intentional (inter)action, termed Bayesian Theory of Mind (BToM). Third, hypotheses about the …
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