cs.LG（2025-02-18）
📊 共 6 篇论文
🎯 兴趣领域导航
支柱二：RL算法与架构 (RL & Architecture) (3)
支柱九：具身大模型 (Embodied Foundation Models) (2)
支柱一：机器人控制 (Robot Control) (1)
🔬 支柱二：RL算法与架构 (RL & Architecture) (3 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 1 | A Graph-Enhanced Deep-Reinforcement Learning Framework for the Aircraft Landing Problem | 提出一种图增强深度强化学习框架，用于优化飞机着陆调度问题。 | reinforcement learning deep reinforcement learning DRL | ||
| 2 | Multi-Objective Reinforcement Learning for Critical Scenario Generation of Autonomous Vehicles | 提出基于多目标强化学习的MOEQT方法，用于生成自动驾驶车辆的关键场景。 | reinforcement learning | ||
| 3 | Reinforcement Learning for Dynamic Resource Allocation in Optical Networks: Hype or Hope? | 评估强化学习在光网络动态资源分配中的有效性，并提出更强的基准测试方法。 | reinforcement learning |
🔬 支柱九：具身大模型 (Embodied Foundation Models) (2 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 4 | Pruning as a Defense: Reducing Memorization in Large Language Models | 利用剪枝技术减少大语言模型的记忆，提升安全性 | large language model | ||
| 5 | Anomaly Detection in Smart Power Grids with Graph-Regularized MS-SVDD: a Multimodal Subspace Learning Approach | 提出图正则化多模态子空间SVDD，用于智能电网异常检测，提升事件检测的可靠性和及时性。 | multimodal |
🔬 支柱一：机器人控制 (Robot Control) (1 篇)
| # | 题目 | 一句话要点 | 标签 | 🔗 | ⭐ |
|---|---|---|---|---|---|
| 6 | A Survey of Sim-to-Real Methods in RL: Progress, Prospects and Challenges with Foundation Models | 综述：基于强化学习的Sim-to-Real方法，探讨了在基础模型下的进展、前景与挑战。 | sim-to-real reinforcement learning deep reinforcement learning |