| 1 |
HAIM-DRL: Enhanced Human-in-the-loop Reinforcement Learning for Safe and Efficient Autonomous Driving |
提出HAIM-DRL以解决安全高效的自动驾驶问题 |
reinforcement learning deep reinforcement learning DRL |
✅ |
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| 2 |
On Sample-Efficient Offline Reinforcement Learning: Data Diversity, Posterior Sampling, and Beyond |
提出数据多样性概念以提升离线强化学习的样本效率 |
reinforcement learning offline RL offline reinforcement learning |
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| 3 |
SPQR: Controlling Q-ensemble Independence with Spiked Random Model for Reinforcement Learning |
提出SPQR以解决深度强化学习中的过估计偏差问题 |
reinforcement learning deep reinforcement learning offline RL |
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| 4 |
FedTGP: Trainable Global Prototypes with Adaptive-Margin-Enhanced Contrastive Learning for Data and Model Heterogeneity in Federated Learning |
提出FedTGP以解决异构联邦学习中的原型聚合问题 |
contrastive learning |
✅ |
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| 5 |
An Empirical Investigation of Value-Based Multi-objective Reinforcement Learning for Stochastic Environments |
提出基于价值的多目标强化学习以解决随机环境中的优化问题 |
reinforcement learning |
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| 6 |
QoS-Aware Graph Contrastive Learning for Web Service Recommendation |
提出QoS感知图对比学习以解决Web服务推荐中的冷启动问题 |
contrastive learning |
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| 7 |
Semi-supervised learning via DQN for log anomaly detection |
提出DQNLog以解决日志异常检测中的数据利用不足问题 |
reinforcement learning deep reinforcement learning |
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