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 17 of 17 for “"multi-agent learning"”.
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Approaches to multi-agent learning
Systems involving multiple autonomous entities are becoming more and more prominent. Sensor networks, teams of robotic vehicles, and software agents are just a few examples. In order to design these systems, we need methods that allow our agents to autonomously learn and adapt to the changing …
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Neural diversity in multi-agent learning
… lack of work studying behavioural diversity in multi-agent learning is due to traditional approaches constraining the agents' strategies to be identical. This speeds up learning by training a shared policy from all individuals' experiences, but results in the agents becoming behaviourally …
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Communication and generalization in multi-agent learning
Multi-agent learning aims to allow artificial intelligence (AI) agents to learn from interactions with other agents in an environment. However, as AI increasingly integrates into real-world systems, significant challenges arise in how to robustly interact with and communicate with a variety of …
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Graph Neural Networks for Multi-Agent Learning
Over time, machine learning research has placed an increasing emphasis on utilising relational inductive biases. By focusing on the underlying relationships in graph structured data, it has become possible to create models with superior performance and generalisation. Given different graph …
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Distributed multi-agent learning under federated and competitive settings
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01
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Efficient Decentralized Multi-Agent Learning in Asymmetric Bipartite Queuing Systems
We study decentralized multi-agent learning in bipartite queuing systems, a standard model for service systems. In particular, 𝑁 agents request service from 𝐾 servers in a fully decentralized way, i.e, by running the same algorithm without communication. Previous decentralized algorithms are …
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Who, When, How (Not) to Imitate? The Role of Imitation in Collective Intelligence, and Its Implications on the Design of Socio-Technical Systems
… different institutions. Researchers posit social learning as a mechanism for overcoming individual limitations, quickly adapting to environments, passing knowledge across generations, and enabling rapid cumulative cultural evolution. This thesis demonstrates how multi-agent learning (MAL) can …
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Multi-Task Reinforcement Learning: From Single-Agent to Multi-Agent Systems
… of the technology. The ability to develop these multi-task, multi-agent drone systems is limited by the lack of available training environments, as well as deficiencies of multi-task learning due to a phenomenon known as catastrophic forgetting. In this thesis, we present a set of simulation …
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Reinforcement Learning for Mobile Robot Collision Avoidance in Navigation Tasks
… thesis first studies the map-based approach for multiple robots to collectively build environment maps. In this study, a robot following a pre-planned path may encounter unexpected obstacles, such as other moving robots and obstacles inaccurately presented on an environment map. This motivates us …
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Neural MMO: Massively Multiagent Simulation and Learning
Neural MMO is a massively multi-agent environment for reinforcement learning research. It is designed to push the boundaries of environment complexity while maintaining computationally efficiency for academic research. Agents in Neural MMO can forage for a variety of resources, engage in strategic …
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Optimization and Generalization of Minimax Algorithms
… thesis explores minimax formulations of machine learning and multi-agent learning problems, focusing on algorithmic optimization and generalization performance. The first part of the thesis delves into the smooth convex-concave minimax problem, providing a unified analysis of widely used …
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Learning Successful Strategies in Repeated General-sum Games
<p>Many environments in which an agent can use reinforcement learning techniques to learn profitable strategies are affected by other learning agents. These situations can be modeled as general-sum games. When playing repeated general-sum games with other learning agents, the goal of a …
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Toward efficient multi-agent deep reinforcement learning
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-15 without embargo terms
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Distributed Machine Learning in Heterogeneous Edge Networks
… edge. Meanwhile, the complexity of machine learning models has increased significantly, with state-of-the-art models for tasks like natural language processing and computer vision now containing billions of parameters.Distributed machine learning addresses the challenges posed by massive …
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Performance Analysis and Learning Algorithms in Advanced Wireless Networks
… an exponential growth, especially with multimedia traffic becoming the dominant traffic, and such growth is expected to continue in the near future. This unprecedented growth has led to an increasing demand for high-rate wireless communications.Key solutions for addressing such demand …
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Theoretical Foundations for Learning in Games and Dynamic Environments
… many decision-making problems is the presence of multiple agents, often with competing incentives. To understand how agents will act in such situations, it is often productive to compute equilibria, which have the property that no agent can deviate from them and improve their utility. An …
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Stochastic Optimization For Multi-Agent Statistical Learning And Control
… accurate, and affordable complexity statistical learning among networks of autonomous agents. We begin by noting the connection between statistical inference and stochastic programming, and consider extensions of this setup to settings in which a network of agents each observes a local data …