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Posts
Future Blog Post
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How it works: why use KL divergence as policy constrait? An information theory perspective.
Published:
KL divergence has been long used as a policy constrait in the field of reinforcement learning (RL). For example, in online RL, where agents interacts with the environment to update its policy, KL divergence is adopted to limit the search steps. Actually, KL divergence are so widely in the RL that it has become the golden standard. However, it sounds magical to me: why we adopt KL divergence as the constrait of policies?
portfolio
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publications
Mean-field-aided multiagent reinforcement learning for resource allocation in vehicular networks
Published in IEEE Internet of Things Journal, 2022
This paper is about the application of mean-field reinforcement learning in V2X system.
Recommended citation: Zhang, Hengxi, et al. "Mean-field-aided multiagent reinforcement learning for resource allocation in vehicular networks." IEEE Internet of Things Journal 10.3 (2022): 2667-2679.
Autonomous Swarm Robot Coordination via Mean-Field Control Embedding Multi-Agent Reinforcement Learning
Published in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023
This paper is about the application of mean-field reinforcement learning in swarm robotics.
Recommended citation: Tang, Huaze, et al. "Autonomous Swarm Robot Coordination via Mean-Field Control Embedding Multi-Agent Reinforcement Learning." 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2023.
M^3ARL: Moment-Embedded Mean-Field Multi-Agent Reinforcement Learning for Continuous Action Space
Published in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2024
This paper is about the repesentation of mean-field in continuous action space.
Recommended citation: Tang, Huaze, et al. "M3ARL: Moment-Embedded Mean-Field Multi-Agent Reinforcement Learning for Continuous Action Space." ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2024.
Mean-Field Aided QMIX: A Scalable and Flexible Q-Learning Approach for Large-Scale Agent Groups
Published in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2025
This paper is about the repesentation of mean-field for QMIX algorithm
Recommended citation: Zhang, Enze, Tang, Huaze, et al. "Mean-Field Aided QMIX: A Scalable and Flexible Q-Learning Approach for Large-Scale Agent Groups." ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2025.
talks
Conference Proceeding talk at IROS 2024
Published:
Talk on paper Autonomous Swarm Robot Coordination via Mean-Field Control Embedding Multi-Agent Reinforcement Learning.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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