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 12 of 12 for “"multi-agent RL"”.
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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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Team Learning from Human Demonstration with Coordination Confidence
… proposed to speed-up reinforcement learning (RL), learn- ing from human demonstration has a proven record of success. A related technique, called Human Agent Transfer (HAT), and its confidence-based derivatives have been successfully applied to single agent RL. This paper investigates their …
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Reinforcement learning for multi-agent and robust control systems
… accomplishments of reinforcement learning (RL) in many prominent sequential decision-making problems, such as playing the game of Go, playing real-time strategy games, robotic control, and autonomous driving. Motivated by these empirical successes, research toward theoretical understandings …
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Near-Optimal Learning in Sequential Games
… is ubiquitous, and some problems become particularly challenging due to their sequential nature, where later decisions depend on earlier ones. While humans have been attempting to solve sequential decision making problems for a long time, modern computational and machine learning techniques are …
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Theoretical Foundations for Learning in Games and Dynamic Environments
… systems to interactions with the physical world. A central challenge that arises across 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 …
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SAFE REINFORCEMENT LEARNING-BASED GREEN LIGHT OPTIMAL SPEED ADVISORY FOR MIXED-TRAFFIC PLATOONS
This thesis develops a platoon-centric, safe RL-based Green Light Optimal Speed Advisory (GLOSA) system to optimize the CAV speed profile of a mixed-traffic platoon. First, we design a multi-agent RL algorithm to achieve a balance between the energy and travel efficiency of a mixed-traffic platoon, …
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Explanations for Autonomous Agents
… years have seen an accelerated development of agents and systems capable of sophisticated autonomous behaviour. As the consequences of such agents' actions begin to manifest in society, the need for understanding their decisions motivates the study of mechanisms for obtaining explanations that …
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Addressing deep reinforcement learning: empirical algorithm performance evaluations∗
… paced production of deep reinforcement learning (RL) research papers, some recent publications have begun to critique the manner in which RL algorithm performances are evaluated. Building on this recent scrutiny, our work attempts to identify the precise aspects of empirical deep RL algorithm …
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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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Model-free reinforcement learning in non-stationary Markov Decision Processes
Reinforcement learning (RL) studies the problem where an agent maximizes its cumulative reward through sequential interactions with an initially unknown environment, usually modeled by a Markov Decision Process (MDP). The classical RL literature typically assumes that the state transition functions …
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Online Reinforcement Learning in Factored Markov Decision Processes and Unknown Markov Games
Reinforcement learning (RL) has gained an increasing interest in recent years, being expected to deliver autonomous agents that can learn to interact with an environment. So far the empirical successes rely heavily on enormous amount of data collected during interaction, hence mostly limited to …
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Collaborative embodied agents
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2022-11-11 without embargo terms