标签：一万 十二月 二十二日 Learning Reinforcement https org com Driving

十二月二十二日（一万小时计划）

待读论文

：https://arxiv.org/abs/1911.10868

Decision-Making Strategy on Highwayfor Autonomous Vehicles Using DeepReinforcement Learning

Machine Learning for Cooperative Driving in a Multi-Lane Highway Environment：https://ieeexplore.ieee.org/document/8734192

Deep Distributional Reinforcement Learning Based High-Level Driving Policy Determination：https://ieeexplore.ieee.org/document/8723635

Autonomous Highway Driving using Deep Reinforcement Learning：https://ieeexplore.ieee.org/document/8914621

Speed Planning for Autonomous Driving in Dynamic Urban Driving Scenarios：https://ieeexplore.ieee.org/abstract/document/9235659?casa_token=oy_4-o8EYYsAAAAA:BB-XmKEQ94bne-wxxGRGfc-fC95P5BJ0CH9BxyWPyPYC3MVcOTXCswza2zGHIo6k9EK3dErGuA

Highway Environment Model for Reinforcement Learning：https://www.sciencedirect.com/science/article/pii/S2405896318333032

Deep Reinforcement Learning framework for Autonomous Driving：https://www.ingentaconnect.com/content/ist/ei/2017/00002017/00000019/art00012?cf_chl_jschl_tk=bfdcf529be9afbcd814e9669e0790110c1d53a9b-1608609774-0-AZ4oaHQKFPvVROXJ_YC73Hps3n5j0qHMKU8kAZOSr6p7BDQ23KlKZNllRAuNfeuGQpxBdrsdIzCf0Gc6tUDi0uMyxPu1rS_Wl2ceG0P5WXy74yTH1Wm-B9uz86KwzTDHjNuBB23svu0LYdoqJZn8FKR1gFwfT3Rpt9ZcLQ-mUJqHEbzDmFV3QzL-6wKb7E5wYmYg6_npUn_zAjzVa5UK81eqasWgNi2j2bUp0eJOtJocvBqsci_CEiCsy98ZMxi4IwBJyFWnxdQnLLXW8dgze74lIsb5eupTxuBv_vFHrdfSg_LMw-ym-KBNDYnIbfk0EM_NWdF55OHxdN-p-K-M1qZytRgSKQATl4r_9F1dPo70

How To Create Your Own Reinforcement Learning Environments | Tutorial | Part 1：https://www.youtube.com/watch?v=vmrqpHldAQ0&ab_channel=MachineLearningwithPhil&t=9s

torcsrl：https://www.youtube.com/watch?v=lV5JhxsrSH8&ab_channel=JeroenvandenHeuvel&t=0s

Learning To Follow Directions in Street View：https://paperswithcode.com/paper/learning-to-follow-directions-in-street-view

Self Driving RC Car using Behavioral Cloning：https://arxiv.org/abs/1910.06734

Deep learning algorithm for autonomous driving using GoogLeNet：https://ieeexplore.ieee.org/abstract/document/7995703?casa_token=p9S36hjjGDMAAAAA:E0fyF0u8kw_EBQ0rnb5hxmQrehvMm2Mm3RzYZPzfhvebAhb-ALfhSSgxz-2Jvq3-nakIB3OJkA

Towards self-driving car using convolutional neural network and road lane detector：https://ieeexplore.ieee.org/abstract/document/8253388

Autonomous reinforcement learning on raw visual input data in a real world application：https://ieeexplore.ieee.org/abstract/document/6252823?casa_token=Y1MOda6u2xcAAAAA:6Dh84lmLl1HuwYdYKKRUaLqxI-R8nv152aMuLsJsd2WFWqLUHUzsJ5v1wkog_mFOgOAtSR2XaA

DeepWay: a Deep Learning Estimator for Unmanned Ground Vehicle Global Path Planning：https://arxiv.org/abs/2010.16322

A Survey on Visual Navigation for Artificial Agents With Deep Reinforcement Learning：https://ieeexplore.ieee.org/abstract/document/9146614

Path Planning via an Improved DQN-Based Learning Policy：https://ieeexplore.ieee.org/abstract/document/8721655

SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving：https://paperswithcode.com/paper/smarts-scalable-multi-agent-reinforcement

Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement Learning：https://paperswithcode.com/paper/interpretable-end-to-end-urban-autonomous

Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty Estimation：https://paperswithcode.com/paper/tactical-decision-making-in-autonomous

A Hierarchical Architecture for Sequential Decision-Making in Autonomous Driving using Deep Reinforcement Learning：https://paperswithcode.com/paper/a-hierarchical-architecture-for-sequential

Real-time Multi-target Path Prediction and Planning for Autonomous Driving aided by FCN：https://paperswithcode.com/paper/real-time-multi-target-path-prediction-and

One Thousand and One Hours: Self-driving Motion Prediction Dataset：https://paperswithcode.com/paper/one-thousand-and-one-hours-self-driving

Multi-Agent Connected Autonomous Driving using Deep Reinforcement Learning：https://paperswithcode.com/paper/multi-agent-connected-autonomous-driving

Asynchronous Methods for Deep Reinforcement Learning: TORCS

Asynchronous Methods for Deep Reinforcement Learning: Labyrinth：YouTube

Urban Driving with Conditional Imitation Learning：https://ieeexplore.ieee.org/abstract/document/9197408?casa_token=KXH7Ww7-Zy8AAAAA:LhlEYbaEVdD81K_zgWcQOYiZJG0pozqS6OSL_tf_9S6KQVoy4MfTFiKOmAeq7KjFOzDTLLqCvQ

A Survey of Motion Planning and Control Techniques for Self-Driving Urban Vehicles：https://ieeexplore.ieee.org/abstract/document/7490340?casa_token=wWgdmpqYcwEAAAAA:2utJss0HySzlmdQf0vslc4KV_IssPxUXcZ8-7NHfcxnrBMGWoQHO996gOJ50emcdn2RgX0XZYQ

Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning：https://paperswithcode.com/paper/automated-lane-change-strategy-using-proximal

End-to-end Learning of Image based Lane-Change Decision：https://paperswithcode.com/paper/end-to-end-learning-of-image-based-lane

Efficient Motion Planning for Automated Lane Change based on Imitation Learning and Mixed-Integer Optimization：https://paperswithcode.com/paper/a-data-driven-approach-for-motion-planning-of

A Survey on Visual Traffic Simulation: Models, Evaluations, and Applications in Autonomous Driving：https://onlinelibrary.wiley.com/doi/full/10.1111/cgf.13803

a survey：https://onlinelibrary.wiley.com/doi/epdf/10.1002/rob.21918

理论

决策规划：http://www.iitraffic.com/index.php?c=msg&id=2470&

代码

Autopilot-TensorFlow

Path-Planner-Using-Reinforcement-Learning

[carla_cil_pytorch](

标签：一万,十二月,二十二日,Learning,Reinforcement,https,org,com,Driving
来源： https://www.cnblogs.com/ethancode/p/14172294.html

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