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 22 for “"Inverse reinforcement learning"”.
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Adversarial Inverse Reinforcement Learning with Noisy Observations
<p>Inverse reinforcement learning (IRL) has emerged as a popular approach for training robots from human/expert demonstration, where a learner/robot infers the expert's hidden reward function using the demonstrations and a simulator. We argue that noise is inevitable in certain parts of the …
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Inverse Reinforcement Learning and Routing Metric Discovery
… This thesis presents a method for utilizing inverse reinforcement learning (IRL)techniques for the purpose of discovering a composite metric used by a dynamic routing algorithm on an Internet Protocol (IP) network. The network and routing algorithm are modeled as a reinforcement learning (RL) …
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TOWARDS HUMAN-CENTRIC AI: INVERSE REINFORCEMENT LEARNING MEETS ALGORITHMIC FAIRNESS
… alignment and fairness. Our first work explores Inverse reinforcement learning (IRL), a potential solution to value alignment. We introduce BO-IRL, an IRL algorithm that uses a novel kernel to explore the reward function space efficiently. The second work introduces SCALES, a framework that …
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Faster apprenticeship learning through inverse optimal control
… problems of artificial intelligence is learning how to behave optimally. With applications ranging from self-driving cars to medical devices, this task is vital to modern society. There are two complementary problems in this area – reinforcement learning and inverse reinforcement …
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Modeling users' powertrain preferences
… determine the driver's preferences. A supervised learning approach has already been applied to this problem. However, because the approach locally classify a small interval at a time and is memoryless, the supervised learning does not perform well on our goal. Instead, we need to introduce new …
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Terrain Cost Learning from Human Preferences for Robot Path Planning Using a Visual User Interface
… thesis aims to achieve this goal by employing an inverse reinforcement learning (IRL) framework based on prior work in preference-based inverse reinforcement learning (PbIRL) [21] in conjunction with a visual user interface. In this work, the agent obtains relative preferences between two …
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Bayesian nonparametric reward learning from demonstration
Learning from demonstration provides an attractive solution to the problem of teaching autonomous systems how to perform complex tasks. Demonstration opens autonomy development to non-experts and is an intuitive means of communication for humans, who naturally use demonstration to teach others. …
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Imitation Learning with Superhuman Policy Gradient Optimization for Sequential Cancer Treatment Decisions
We propose a simulator-driven imitation learning framework for sequential deci- sion making in head and neck cancer (HNC) treatment. Our method, Superhu- man Policy Gradient Optimization (SPGO), integrates inverse reinforcement learning principles with policy gradient updates to derive three-stage …
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Data-Driven Routing for Autonomous Trucks: Learning from Human Behavior with Context Awareness and Privacy Protection
… specifically on these data-driven and behavior-learning challenges, not on hardware or sensor-level issues. This work presents a unified framework for smart route planning for autonomous heavy-duty trucks and pursues three technical objectives: (i) accurate map matching under sparse GPS …
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Inferring the Human's Objective in Human Robot Interaction
… inferring upon the human's objective for Reward Learning and Communicative Shared Autonomy settings. To accomplish this, we first examine state-of-the-art methods for approaching Bayesian Inverse Reinforcement learning where we explore the strengths and weaknesses of current approaches. After …
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Knowledge and Ignorance in Reinforcement Learning
The field of Reinforcement Learning is concerned with teaching agents to take optimal decisions to maximize their total utility in complicated environments. A Reinforcement Learning problem, generally described by the Markov Decision Process formalism, has several complex interacting components, …
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Irreversible Actions in Assistance Games with a Dynamic Goal
Reinforcement Learning (RL) agents optimize reward functions to learn desirable policies in a variety of important real-world applications such as self-driving cars and recommender systems. However, in practice, it can be very difficult to specify the correct reward function for a complex problem, …
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The Science of Mind Reading: New Inverse Optimal Control Framework
… model which results in the actions. Using reinforcement learning algorithms, we solve the forward problem of solving for the optimal actions given a model and a given reward function. We then propose a novel framework of inverse reinforcement learning, which learns optimal policies …
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Autonomous aggressive driving: theory & experiments
… sedan, etc.). This allows us to solve the (inverse) reinforcement learning problem efficiently since the dimensionality of the state space can be maintained in a manageable level. New path planning algorithms using Bezier curves are proposed to generate everywhere 𝐶2 continuous …
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Offline Reward Learning from Human Demonstrations and Feedback: A Linear Programming Approach
… this data, two key methodologies have emerged: inverse reinforcement learning (IRL) and reinforcement learning from human feedback (RLHF). Despite the successful application of these reward learning techniques across a wide range of tasks, a significant gap between theory and practice persists. …
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Bayesian computational models for inferring preferences
This thesis is about learning the preferences of humans from observations of their choices. It builds on work in economics and decision theory (e.g. utility theory, revealed preference, utilities over bundles), Machine Learning (inverse reinforcement learning), and cognitive science (theory of mind …
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An Intent-based Neural Monte Carlo Tree Search Framework for Synthesis of Printed Circuit Boards
… datasets, culminating in a process called LFS (Learning Feedback System). This process allows using past data to accelerate MCTS with deep RL models on new or similar board configurations. Datasets are utilized with forms of dataset-based Reinforcement Learning (RL) algorithms, known as …
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Aligning Models for Human-Centric Decision Systems
When employed for important decisions, machine learning models do not operate in a vacuum. While they have achieved impressive results across a variety of tasks, they are by no means faultless in their decisions and, without any responsibility or accountability on the part of algorithms, human …
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Dynamic Discrete Choice Estimation using Reinforcement Learning with Applications in Online Food Markets
… consumer datasets. This thesis develops Reinforcement Learning (RL)-based estimation methods to improve the speed and scalability of DDC estimation. The second chapter establishes a theoretical foundation for integrating RL with DDC estimation, emphasizing the shared mathematical …
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Adaptive Planning in Changing Policies and Environments
… able to adapt to different tasks is a staple of learning, as agents aim to generalize across different situations. Specifically, it is important for agents to adapt to the policies of other agents around them. In swarm settings, multi-agent sports settings, or other team-based environments, …
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