《Virtual to Real Reinforcement Learning for Autonomous Driving》翻譯
《Virtual to Real Reinforcement Learning for Autonomous Driving》 摘要：強化學習被認爲是推動政策學習的一個有前景的方向。然而，在實際環境中，用強化學習訓練自動駕駛車輛涉及到無法負擔的試錯。最好先在虛擬環境中培訓，然後再轉移到真實環境中。本文提出了一種新穎的現實翻譯網絡，使在虛擬環境中訓練的翻譯模型在現實世界中具有可操作性。該網絡可以將非
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相關文章
- 1. YUVMultiNet Real-time YUV multi-task CNN for autonomous driving
- 2. 行文克隆（End to End Learning for Self-Driving Cars 論文翻譯）
- 3. Pillar-based Object Detection for Autonomous Driving
- 4. 論文閱讀之Virtual-to-real Deep Reinforcement Learning
- 5. MV3D：Multi-View 3D Object Detection Network for Autonomous Driving（翻譯）
- 6. 【閱讀】-- PillarFlow : End-to-end Birds-eye-view Flow Estimation for Autonomous Driving
- 7. Virtual-to-real DRL: Continuous Control of Mobile Robots for Mapless Navigation
- 8. 論文筆記：MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
- 9. Building a ROS simulation platform for Deep Reinforcement Learning research ...
- 10. Learning to Design Games Strategic Environments in Reinforcement Learning（部分翻譯）