[文献精读] End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Affordances
End-to-End Model-Free Reinforcement Learning for Urban Driving using Implicit Afforda 摘要 1 引言 2 相关工作 2.1 用RL实现端对端无人驾驶 2.2 辅助任务和学习能力 3 Carla的挑战 4. 方法 4.1 RL设置 Rainbow-IQN Ape-X 4.2 奖励设计（reward shaping）
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相关文章
- 1. [文献精读] Summit:A Simulator for Urban Driving
- 2. 《Virtual to Real Reinforcement Learning for Autonomous Driving》翻译
- 3. Language Understanding for TextGames using Deep Reinforcement
- 4. Prototype Rectification for Few-shot Learning 文献阅读
- 5. 文献阅读 - Deep Residual Learning for Image Recognition
- 6. 论文阅读笔记（五十二）：Outline Objects using Deep Reinforcement Learning
- 7. 读文献——《Curriculum learning》
- 8. 论文笔记4：Language Understanding for Text-based Games using Deep Reinforcement Learning
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- 10. 阅读Qatten：A General Framework for Cooperative Multiagent Reinforcement Learning