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 7 of 7 for “"Multi-Agent Deep Reinforcement Learning"”.
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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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MULTI-AGENT DEEP REINFORCEMENT LEARNING AND HYBRID SIMULATION FOR RESOURCE ALLOCATION IN PORT OPERATIONS
… a hybrid simulation framework integrating multi-agent deep reinforcement learning (MADRL) for port operations. Unlike traditional methods that optimize berths, AGVs, and Yard Blocks separately, our approach treats them as interconnected agents to enable collaborative optimization. The …
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Multi-Agent Deep Reinforcement Learning and GAN-Based Market Simulation for Derivatives Pricing and Dynamic Hedging
… in computing capabilities have enabled machine learning algorithms to learn directly from large amounts of data. Deep reinforcement learning is a particularly powerful method that uses agents to learn by interacting with an environment of data. Although many traders and investment managers rely …
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Hybrid AI-driven Approach to Context-Aware Inter-Slice Load Balancing for Cloud-Native Functions in 5G Networks
… custom execution environments (EEs), enabling AI-agent interactions for intelligent capabilities. Graph neural networks (GNN) and spatio-temporal multi-head graph attention networks (SP-mGAT) are utilized to generate context-aware embeddings, clustering clients by traffic characteristics into …
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Security of Communication-Based Train Control Systems
… detection systems (IDSs) that use machine learning (ML) to detect attacks on train-to-wayside (T2W) communications. It facilitates the development of adaptive ML-based IDSs responsive to changes in the dynamic environmental context. The experimental results show that context awareness …
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Collaborative Autonomy of Multi-Agent Aerial Systems for Future Disaster Response
… cooperative navigation, resource allocation, and agentic decision-making for multi-agent aerial systems. Chapter 1 provides the research background and motivation for developing collaborative autonomy in multi-agent aerial systems for disaster response applications. Chapter 2 reviews the state of …