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 27 for “"POMDPs"”.
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Computationally Efficient Reinforcement Learning under Partial Observability
… Partially observable Markov decision processes (POMDPs) are a generalization of Markov decision processes (MDPs) that model this challenge. Unfortunately, planning and learning near-optimal policies in POMDPs is computationally intractable. Most existing algorithms either lack provable …
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Particle Filtering for Stochastic Control and Global Optimization
… partially observable Markov decision processes (POMDPs), provides an ideal paradigm to model discrete-time sequential decision making under uncertainty and partial observation. However, POMDPs usually do not admit analytical solutions, and are computationally very expensive to solve most of the …
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Approximate solution methods for partially observable Markov and semi-Markov decision processes
… lower cost approximation method for finite-space POMDPs with the average cost criterion, and its extensions to semi-Markov partially observable problems and constrained POMDP problems, as well as to problems with the undiscounted total cost criterion. Our method is an extension of several lower …
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Dealing with uncertainty : a comparison of robust optimization and partially observable Markov decision processes
… partially observable Markov decision processes (POMDPs) are two methods of dealing with uncertainty in real life problems. Robust optimization is used primarily in operations research, while engineers will be more familiar with POMDPs. For a decision maker who is unfamiliar with one or both of …
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Efficient model learning for dialog management
Partially Observable Markov Decision Processes (POMDPs) have succeeded in many planning domains because they can optimally trade between actions that will increase an agent's knowledge about its environment and actions that will increase an agent's reward. However, POMDPs are defined with a large …
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Intelligent Knowledge Distribution for Multi-Agent Communication, Planning, and Learning
… Distribution (IKD), including Constrained-action POMDPs (CA-POMDP) and concurrent decentralized (CoDec) POMDPs for an agnostic plug-and-play capability for fully autonomous systems. Each agent runs a CoDec POMDP where all the decision making (motion planning, task allocation, asset monitoring, and …
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Autonomous and Responsive Surveillance Network Management for Adaptive Space Situational Awareness
… Partially Observed Markov Decision Processes (POMDPs) with covariance inflation or multiple model adaptive estimation techniques to task sensors and maintain viable orbit estimates for all targets. The POMDPs developed in this dissertation use information-based and system-based metrics to …
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Exploiting insensitivity in stochastic systems to learn approximately optimal policies
… partially observed Markov decision processes (POMDPs). The techniques presented herein exploit the inherent insensitivity in POMDPs based on the notion that small changes in a policy have little impact on the quality of the solution except at a small set of critical points. First, a …
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Decentralized control of multi-robot systems using partially observable Markov Decision Processes and belief space macro-actions
… Observable Markov Decision Processes (Dec-POMDPs) are general models for multi-robot coordination problems. However, representing and solving Dec-POMDPs is often intractable for large problems. This thesis extends the Dec-POMDP framework to the Decentralized Partially Observable Semi-Markov …
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On the design and implementation of decision-theoretic, interactive, and vision-driven mobile robots
… Partially Observable Markov Decision Processes (POMDPs) that remove the latter assumption and allow us to model the uncertainty in sensor measurements. The MSMDP model utilizes a divide-and-conquer approach for solving problems with millions of states using concurrent actions. For solving large …
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Optimization for stochastic, partially observed systems using a sampling-based approach to learn switched policies
… partially observable Markov decision processes (POMDPs) that require long time horizons for planning. Computing optimal policies for POMDPs is an intractable problem and, in practice, dimensionality renders exact solutions essentially unreachable for even small real-world systems of interest. For …
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Advancing Efficiency and Safety in Autonomous Sequential Decision Making
… partially observable Markov decision processes (POMDPs), which represent an agent's uncertainty about its environmental state. A novel framework that integrates active inference (AIF), based on the free energy principle, with RL in continuous-space POMDPs is introduced. By emphasizing the …
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Balancing exploration and exploitation: task-targeted exploration for scientific decision-making
… partially observable Markov decision processes (POMDPs) and present two novel planners that leverage task-driven information measures to balance exploration and exploitation. These planners drive robots in simulation and oceanographic field trials to robustly identify plume sources and track …
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Incremental sampling based algorithms for state estimation
… partially observable Markov decision processes (POMDPs), in such a way that the trajectories of the POMDPs -- i.e., the trajectories of beliefs -- converge to the trajectories of the original continuous problem. Modern point-based solvers are used to approximate control policies for each of these …
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Balancing Exploration and Exploitation: Task-Targeted Exploration for Scientific Decision-Making
… partially observable Markov decision processes (POMDPs) and present two novel planners that leverage task-driven information measures to balance exploration and exploitation. These planners drive robots in simulation and oceanographic field trials to robustly identify plume sources and track …
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Symbolic planning in belief space
… be applied to them. SASY is optimized to solve POMDPs encoded in belief space symbolic formalism, but can also be used to find a solution to general symbolic planning problems. We compare SASY to two other POMDP solvers, SARSOP and POMDPX_NUS, and define a new benchmark domain called Elevator.
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Aircraft collision avoidance using Monte Carlo Real-Time Belief Space Search
… partially observable Markov decision processes (POMDPs). MC-RTBSS combines a sample-based belief state representation with a branch and bound pruning method to search through the belief space for the optimal policy. The algorithm is applied to the problem of aircraft collision avoidance and its …
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Compact parametric models for efficient sequential decision making in high-dimensional, uncertain domains
… partially observable Markov decision processes (POMDPs), when the agent knows the dynamics and reward models, but only receives information about its state through its potentially noisy sensors. One of the key challenges in the sequential decision making field is the tradeoff between optimality …
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Knowledge and Ignorance in Reinforcement Learning
… In this work, I describe how in certain cases POMDPs can be approximated by MDPs or slightly more complicated models with bounded performance loss. I also present an algorithm, called Smoothed Q-Learning for learning policies when the observation models are uncertain. Smoothed Sarsa is based on …
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Bayesian Theory of Mind : modeling human reasoning about beliefs, desires, goals, and social relations
… 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 structure and content of ToM can be …
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