Deep Q-Learning

Overview of Multi-Agent Reinforcement Learning: Principles, Algorithms, and Challenges

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Overview of Multi-Agent Reinforcement Learning: Principles, Algorithms, and Challenges

1. Introduction Multi-Agent Reinforcement Learning (MARL) is an important branch of reinforcement learning that extends the traditional single-agent reinforcement learning concept to multi-agent environments. In MARL, multiple agents learn optimal strategies through interactions with the environment and other agents to maximize cumulative rewards in cooperative or competitive scenarios. Adversarial interactions among agents in MAgent (mixed … Read more

Q-Learning Based Joint Offloading Strategy for C-V2X Edge Computing

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Q-Learning Based Joint Offloading Strategy for C-V2X Edge Computing

The team led by Professor Ai Bo from the School of Electronic Information Engineering at Beijing Jiaotong University proposed an offloading method based on cellular vehicular networks, building on previous research in vehicular edge computing. The relevant research results were published in the “Acta Electronica Sinica” 2024, Issue 2, under the title “Q-Learning Based Joint … Read more

Q-Learning UANET Routing Protocol Based on Link Quality and Node Load Estimation

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Q-Learning UANET Routing Protocol Based on Link Quality and Node Load Estimation

Table of Contents for Issue 10 of 2023 | Special Topic: Mobile Ad Hoc Networks Improved LAR Routing Method Based on Distance Threshold Correction in 6G Drone Self-Organizing Networks A Geographical Routing Algorithm for Drone-Assisted Surface Self-Organizing Networks Based on Location Prediction “Mobile Self-Organization” Special Topic · 03 Q-Learning UANET Routing Protocol Based on Link … Read more

Car Control and Trajectory Planning Based on Deep Q-Learning and Bicycle Dynamics Model MATLAB Code

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Car Control and Trajectory Planning Based on Deep Q-Learning and Bicycle Dynamics Model MATLAB Code

🔥 Content Introduction The rapid development of autonomous driving technology has raised higher demands for precise and efficient vehicle control and trajectory planning. Traditional control methods often rely on pre-designed rules and complex mathematical models, making it difficult to cope with the complex and changing road environment. In recent years, the emergence of deep reinforcement … Read more

Research on Resource Allocation for Dynamic Spectrum Access in Cognitive Radio Networks Based on Reinforcement Learning (Q-Learning)

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Research on Resource Allocation for Dynamic Spectrum Access in Cognitive Radio Networks Based on Reinforcement Learning (Q-Learning)

Gift to Readers Conducting research involves a profound system of thought, requiring researchers to be logical, meticulous, and earnest. However, effort alone is not enough; leveraging resources is often more important. Additionally, one must have innovative and inspirational points of view. It is recommended that readers browse through the content in order to avoid suddenly … Read more