[文獻精讀] 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 相關工作 2.1 用RL實現端對端無人駕駛 2.2 輔助任務和學習能力 3 Carla的挑戰 4. 方法 4.1 RL設置 Rainbow-IQN Ape-X 4.2 獎勵設計（reward shaping）
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相關文章
- 1. [文獻精讀] Summit:A Simulator for Urban Driving
- 2. 《Virtual to Real Reinforcement Learning for Autonomous Driving》翻譯
- 3. Language Understanding for TextGames using Deep Reinforcement
- 4. Prototype Rectification for Few-shot Learning 文獻閱讀
- 5. 文獻閱讀 - Deep Residual Learning for Image Recognition
- 6. 論文閱讀筆記（五十二）：Outline Objects using Deep Reinforcement Learning
- 7. 讀文獻——《Curriculum learning》
- 8. 論文筆記4：Language Understanding for Text-based Games using Deep Reinforcement Learning
- 9. 論文：Robotic Arm Representation Using Image-Based Feedback for Deep Reinforcement Learning
- 10. 閱讀Qatten：A General Framework for Cooperative Multiagent Reinforcement Learning