《Virtual to Real Reinforcement Learning for Autonomous Driving》翻译
《Virtual to Real Reinforcement Learning for Autonomous Driving》 摘要：强化学习被认为是推动政策学习的一个有前景的方向。然而，在实际环境中，用强化学习训练自动驾驶车辆涉及到无法负担的试错。最好先在虚拟环境中培训，然后再转移到真实环境中。本文提出了一种新颖的现实翻译网络，使在虚拟环境中训练的翻译模型在现实世界中具有可操作性。该网络可以将非
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相关文章
- 1. YUVMultiNet Real-time YUV multi-task CNN for autonomous driving
- 2. 行文克隆（End to End Learning for Self-Driving Cars 论文翻译）
- 3. Pillar-based Object Detection for Autonomous Driving
- 4. 论文阅读之Virtual-to-real Deep Reinforcement Learning
- 5. MV3D：Multi-View 3D Object Detection Network for Autonomous Driving（翻译）
- 6. 【阅读】-- 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. 论文笔记：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（部分翻译）