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 45 for “"multi-agent reinforcement learning"”.
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Multi-Agent Reinforcement Learning for Intrusion Detection
… any size. To address this problem we propose a Multi-Agent Reinforcement Learning (MARL) approach. In Reinforcement Learning (RL) agents learn to act optimally via observations and feedback from the environment in the form of positive or negative rewards. The thesis also investigates new methods …
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Multi-Agent Reinforcement Learning for Autonomous Robotics
… challenges. The impact of integrating robotic agents into real-world applications may be significantly enhanced by leveraging advancements in multi-agent autonomous systems. However, the coordination required in multi-agent systems demands complex motion planning to deconflict actions and …
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Towards a unified multi-agent reinforcement learning framework
The field of Multi-Agent Reinforcement Learning (MARL) has rapidly evolved, yet integrating diverse tasks and algorithms into a cohesive system remains a complex challenge. This thesis proposes a unified framework aimed at improving adaptability, scalability, and cooperative dynamics among agents …
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Multi-agent reinforcement learning: A mean-field perspective
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms
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Designing policy optimization algorithms for multi-agent reinforcement learning
Multi-agent reinforcement learning (RL) studies the sequential decision-making problem in the setting where multiple agents exist in an environment and jointly determine the environment transition. The relationship between the agents can be cooperative, competitive, or mixed depending on how the …
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Multi-agent reinforcement learning for nonzero-sum Markov games
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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Analyzing Multi-Agent Reinforcement Learning and Coevolution in Cybersecurity Simulations
… can offer deep insights into the behavior of agents in the battle to secure computer systems. We build on existing work modeling the competition between an attacker and defender on a network architecture in a zero-sum game using a graph database linking cybersecurity attack patterns, …
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Decentralized graph-based multi-agent reinforcement learning for traffic signal optimization
… develops a decentralized graph-based multi-agent reinforcement learning (DGMARL) framework for adaptive traffic signal control. The framework advances the state of the art by (i) embedding operational constraints, including minimum/maximum green durations, pedestrian recalls, and …
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On Multi-Agent Reinforcement Learning in Matrix, Stochastic and Differential Games
In this thesis, we investigate how reinforcement learning algorithms can be applied to two different types of games. The first type of games are matrix and stochastic games, where the states and actions are represented in discrete domains. In this type of games, we propose two multi-agent …
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Autonomous UAV positioning using multi-agent reinforcement learning with decentralized swarms
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-05-01
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Mitigating Social Dilemmas in Multi-Agent Reinforcement Learning with Formal Contracting
… more sophisticated artificial intelligence (AI) agents, it will be increasingly necessary for such agents, while pursuing their own objectives, to coexist in common environments in the physical or digital worlds. This may pose a challenge if the agents’ objectives conflict with each other– in the …
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Cooperate to compete : composable planning and inference in multi-agent reinforcement learning
Cooperation within a competitive social situation is a essential part of human social life. This requires knowledge of teams and goals as well as an ability to infer the intentions of both teammates and opponents from sparse and noisy observations of their behavior. We describe a formal generative …
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FlightMARL: A Multi-Agent Reinforcement Learning Framework for Vision-Based Control of Autonomous Quadrotors
… Flightmare simulator that implements a modular multi-agent reinforcement learning engine capable of supporting an array of models and algorithms. We explore representation learning of various different models in this environment. We implement recurrent agents as both discrete and continuous …
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Random Access Control In Massive Cellular Internet of Things: A Multi-Agent Reinforcement Learning Approach
… evenly over time under bursty traffic. Second, a multi-agent reinforcement learning based preamble selection framework is designed to increase the access capacity under a fixed number of preambles. Combining the two mechanisms provides superior performance under various 3GPP-specified machine type …
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Network-aware Multi-agent Reinforcement Learning for Adaptive Navigation of Vehicles in a Dynamic Road Network
… congestion problem, we propose a network-aware multi-agent reinforcement learning (MARL) model for the navigation of a fleet of vehicles in the road network. The proposed model is adaptive to the current traffic conditions of the road network. The main idea is that a Reinforcement Learning (RL) …
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Warm-Starting Networks for Sample-Efficient Continuous Adaptation to Parameter Perturbations in Multi-Agent Reinforcement Learning
Deep reinforcement learning (RL) methods have made significant advancements over recent years toward mastering challenging problems. Because many real-world systems involve multiple agents interacting with each other in a shared environment, one particularly active subfield of RL is multi-agent …
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The impact of market structure on price determination : a simulation approach using multi-agent reinforcement learning in continuous state and action space
… adaptive market simulation system consists of multiple agents and a centralized exchange. By applying reinforcement learning techniques, agents evolve and become capable of making intelligent trading decisions while adapting to changing market conditions. Trading dynamics in the real world are …
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On Solving Larger Games: Designing New Algorithms Adaptable to Deep Reinforcement Learning
… focus on developing algorithms suitable for deep reinforcement learning in two-player zero-sum extensive-form games. There are three critical properties for effective deep multi-agent reinforcement learning: (last/best) iterate convergence, efficient utilization of stochastic trajectory feedback, …
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Deep reinforcement learning in RoboCup Keepaway
The central theme in multi-agent reinforcement learning often involves the coordination of agents to accomplish a common task. However, in complex environ- ments, the agent action space scales exponentially with the number of agents. As a result of this complexity, the coordination graph formalism …
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Social and affective machine learning
Social learning is a crucial component of human intelligence, allowing us to rapidly adapt to new scenarios, learn new tasks, and communicate knowledge that can be built on by others. This dissertation argues that the ability of artificial intelligence to learn, adapt, and generalize to new …
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