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 14 of 14 for “"Hierarchical Reinforcement Learning"”.
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Hierarchical reinforcement learning for adaptive and autonomous decision-making in robotics
In recent years, Reinforcement Learning has been able to solve extremely complex games in simulation, but with limited success in deployment to real-world scenarios. The goal of this work is create an ecosystem in which Reinforcement Learning algorithms can be deployed onto real robots in complex …
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From Static to Adaptive: Dynamic Cost Function Weight Adaptation in Hierarchical Reinforcement Learning for Sustainable 6G Radio Access Networks
… and Quality of Service (QoS) degradation in Deep Reinforcement Learning (DRL)-based BS switching. Using a realistic spatio-temporal dataset, we show that static cost weights lead to suboptimal performance under varying traffic conditions. To address this, we propose a Hierarchical Reinforcement …
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Building Modular, Human-Interpretable AI Systems with Behavior Trees
… experts before. 2) Behavior tree for efficient hierarchical reinforcement learning. Behavior tree provides a modular problem formulation that facilitates searching within feasible actions and re-applying acquired knowledge. Behavior tree augmented reinforcement learning agents are more efficient …
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Integrating Deterministic Planning and Reinforcement Learning for Complex Sequential Decision Making
… algorithm which combines automated planning withhierarchical reinforcement learning in order to take advantage of the model when it is availableand sample from the environment when it is not. We prove that the same guarantees of optimalitythat apply to hierarchical reinforcement learning also …
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Factored State Abstraction for Option Learning
Hierarchical reinforcement learning has focused on discovering temporally extended actions (options) to provide efficient solutions for long-horizon decision-making problems with sparse rewards. One promising approach that learns these options end-toend in this setting is the option-critic (OC) …
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Learning structured representations for perception and control
I argue that the intersection of deep learning, hierarchical reinforcement learning, and generative models provides a promising avenue towards building agents that learn to produce goal-directed behavior given sensations. I present models and algorithms that learn from raw observations and will …
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There Is Always an Option
… into smaller sub-tasks, either to accelerate learning or to enable transfer across related environments, remains a central challenge in reinforcement learning (RL). Hierarchical Reinforcement Learning (HRL) addresses this problem by introducing temporal abstractions, often instantiated as …
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Scalable hierarchical evolution strategies
Hierarchical reinforcement learning (HRL) has been steadily growing in popularity for solving the hardest reinforcement learning problems. However, current HRL algorithms are relatively slow and brittle to hyperparameter changes. This paper offers a solution to these slow and brittle HRL …
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Model-based active learning in hierarchical policies
Hierarchical task decompositions play an essential role in the design of complex simulation and decision systems, such as the ones that arise in video games. Game designers find it very natural to adopt a divide-and-conquer philosophy of specifying hierarchical policies, where decision modules can …
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Learning to Plan by Learning Rules
… everything from scratch. By contrast, deep reinforcement learning (DRL) algorithms are ill-suited to learning policies in rule-based environments, as satisfying rules often involves executing lengthy tasks with sparse rewards. Furthermore, learned DRL policies are difficult if not impossible …
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Predictive Processing Alterations in Psychosis Across Illness Stage, Hierarchical Level and Thematic Domain
… is a domain-general account of the brain as a hierarchical, dynamically evolving Bayesian predictive model: of a volatile, stochastic world whose nested hidden states interactively and probabilistically generate sensory inputs. The brain infers the hidden causes of ambiguous, noisy neural …
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ML-Based Optimization of Large-Scale Systems: Case Study in Smart Microgrids and 5G RAN
The recent advances in machine learning (ML) have brought revolutionary changes to every field. Many novel applications, such as face recognition and natural language processing, have demonstrated the great potential of ML techniques. Indeed, ML can significantly enhance the intelligence of many …
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Human-Robot Interaction with Pose Estimation and Dual-Arm Manipulation Using Artificial Intelligence
… decision-making, this research applies machine learning techniques to human pose estimation, robotic manipulator modeling, and dual-arm robotic manipulation, respectively. In the human pose estimation, an efficient parallel ensemble-based neural network is developed to provide real-time human …
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Generalization in Planning
… the problems. Finally, we show how we can solve reinforcement learning problems where multiple different tasks must be solved in a single environment. As with all reinforcement learning problems, the exact domain dynamics are initially unknown, and solutions must be obtained by repeatedly …