Efficient multi-agent communication via self-supervised information aggregation
Utilizing messages from teammates can improve coordination in cooperative Multi-agent
Reinforcement Learning (MARL). To obtain meaningful information for decision-making …
Reinforcement Learning (MARL). To obtain meaningful information for decision-making …
[PDF][PDF] Multi-Agent Concentrative Coordination with Decentralized Task Representation.
Value-based multi-agent reinforcement learning (MARL) methods hold the promise of
promoting coordination in cooperative settings. Popular MARL methods mainly focus on the …
promoting coordination in cooperative settings. Popular MARL methods mainly focus on the …
Towards explainable traffic signal control for urban networks through genetic programming
The increasing number of vehicles in urban areas draws significant attention to traffic signal
control (TSC), which can enhance the efficiency of the entire network by properly switching …
control (TSC), which can enhance the efficiency of the entire network by properly switching …
Locally-adaptive mapping for network alignment via meta-learning
Network alignment (NA), discovering anchor nodes that represent the same entities across
different networks, plays a fundamental role in information fusion. Most existing embedding …
different networks, plays a fundamental role in information fusion. Most existing embedding …
Heterogeneous interaction modeling with reduced accumulated error for multiagent trajectory prediction
S Chen, J Wang - IEEE Transactions on Neural Networks and …, 2022 - ieeexplore.ieee.org
Dynamical complex systems composed of interactive heterogeneous agents are prevalent in
the world, including urban traffic systems and social networks. Modeling the interactions …
the world, including urban traffic systems and social networks. Modeling the interactions …
[HTML][HTML] Z-Score Experience Replay in Off-Policy Deep Reinforcement Learning
Reinforcement learning, as a machine learning method that does not require pre-training
data, seeks the optimal policy through the continuous interaction between an agent and its …
data, seeks the optimal policy through the continuous interaction between an agent and its …
Multi-agent meta-reinforcement learning with coordination and reward shaping for traffic signal control
X Du, J Wang, S Chen - Pacific-Asia Conference on Knowledge Discovery …, 2023 - Springer
Traffic signal control (TSC) plays an important role in alleviating heavy traffic congestion
problem. It is helpful to provide an effective transportation system by optimizing traffic signals …
problem. It is helpful to provide an effective transportation system by optimizing traffic signals …
Concurrent optimization of safety and traffic flow using deep reinforcement learning for autonomous intersection management
With increasing connectivity and autonomy in traffic eco-systems, Autonomous Intersection
Management (AIM) has attracted strong attention from the research community. AIM helps …
Management (AIM) has attracted strong attention from the research community. AIM helps …
Robust Multi-Agent Reinforcement Learning against Adversaries on Observation
With the broad applications of deep learning, such as image classification, it is becoming
increasingly essential to tackle the vulnerability of neural networks when facing adversarial …
increasingly essential to tackle the vulnerability of neural networks when facing adversarial …
基于潜在状态分布GPT 的离线多智能体强化学习方法.
盛蕾， 陈希亮， 赖俊 - … of Frontiers of Computer Science & …, 2024 - search.ebscohost.com
通过决策Transformer 对基础模型进行离线预训练可以有效地解决在线多智能体强化学习采样
效率低和可扩展性的问题, 但这种生成预训练方法在个体奖励难以定义和数据集不能覆盖最优 …
效率低和可扩展性的问题, 但这种生成预训练方法在个体奖励难以定义和数据集不能覆盖最优 …