每天一篇論文 296/365 Reinforcement Learning Tracking Control for Robotic Manipulator
Reinforcement Learning Tracking Control for Robotic Manipulator With Kernel-Based Dynamic Model 摘要 強化學習（RL）是一種通過與環境交互來獲得最優控制策略的有效學習方法。然而，RL在執行連續控制任務時面臨許多挑戰。本文在不需要了解和學習機械手動力學模型的情況下，提出了一種基於核函數的RL動力學模型。另
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相關文章
- 1. 每天一篇論文 295/365Residual Reinforcement Learning for Robot Control
- 2. 每天一篇論文 289/365Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment
- 3. 論文：Robotic Arm Representation Using Image-Based Feedback for Deep Reinforcement Learning
- 4. [Reinforcement Learning] Model-Free Control
- 5. Deep Reinforcement Learning with Iterative Shift for Visual Tracking
- 6. 每天一篇論文 310/365 Survey of Model-Based Reinforcement Learning Applications on Robotics
- 7. Reinforcement Learning: Model-free control
- 8. 每天一篇論文 350/365 MVP: Unified Motion and Visual Self-Supervised Learning for Large-Scale Robotic Navig
- 9. #每天一篇論文 258/365 Beyond Pixels: Leveraging Geometry and Shape Cues for OnlineMulti-Object Tracking
- 10. Learning Discriminative Model Prediction for Tracking --- 論文翻譯