更改

​​Zhang等人（2020）<ref>{{cite journal| last1 = Wang| first1 = Y.| last2 = Sun| first2 = J.| last3 = He| first3 = H.| last4 = Sun| first4 = C.| title = Deterministic policy gradient with integral compensator for robust quadrotor control| journal = IEEE Transactions on Systems, Man, and Cybernetics: Systems| volume = 50| issue = 10| pages = 3713–3725| year = 2020| doi = 10.1109/TSMC.2020.3002218| publisher = IEEE}}​​</ref>：通过特征迁移策略，将仿真环境训练的DQN模型迁移至真实机器人，解决仿真到现实（Sim-to-Real）的泛化问题。

​​Zhang等人（2020）<ref>{{cite journal| last1 = Wang| first1 = Y.| last2 = Sun| first2 = J.| last3 = He| first3 = H.| last4 = Sun| first4 = C.| title = Deterministic policy gradient with integral compensator for robust quadrotor control| journal = IEEE Transactions on Systems, Man, and Cybernetics: Systems| volume = 50| issue = 10| pages = 3713–3725| year = 2020| doi = 10.1109/TSMC.2020.3002218| publisher = IEEE}}​​</ref>：通过特征迁移策略，将仿真环境训练的DQN模型迁移至真实机器人，解决仿真到现实（Sim-to-Real）的泛化问题。

​​Barron等人（2019）<ref> {{cite conference| last1 = Barron| first1 = T.| last2 = Whitehead| first2 = M.| last3 = Yeung| first3 = A.| title = Deep reinforcement learning in a 3-D blockworld environment| booktitle = Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)| pages = 1–6| year = 2016| publisher = IJCAI/AAAI Press}}​​</ref>​​：在3D虚拟环境中，采用RGB图像输入训练机器人，在复杂自然场景中实现优于深度输入的导航性能。

​​Barron等人（2019）<ref> {{cite conference| last1 = Barron| first1 = T.| last2 = Whitehead| first2 = M.| last3 = Yeung| first3 = A.| title = Deep reinforcement learning in a 3-D blockworld environment| booktitle = Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI)| pages = 1–6| year = 2016| publisher = IJCAI/AAAI Press}}​​</ref>​​：在3D虚拟环境中，采用RGB图像输入训练机器人，在复杂自然场景中实现优于深度输入的导航性能。

​​​​*'''未知环境探索​​'''：

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*'''未知环境探索​​'''：

​​Haora等人（2020）<ref>​​ {{cite journal| last1 = Li| first1 = H.| last2 = Zhang| first2 = Q.| last3 = Zhao| first3 = D.| title = Deep reinforcement learning-based automatic exploration for navigation in unknown environment| journal = IEEE Transactions on Neural Networks and Learning Systems| volume = 31| issue = 6| pages = 2064–2076| year = 2020| doi = 10.1109/TNNLS.2019.2930059| publisher = IEEE}}</ref>​​：提出自适应全卷积Q网络（AFCQN），结合局部地图信息，显著降低未知环境中的导航失败率。

​​Haora等人（2020）<ref>​​ {{cite journal| last1 = Li| first1 = H.| last2 = Zhang| first2 = Q.| last3 = Zhao| first3 = D.| title = Deep reinforcement learning-based automatic exploration for navigation in unknown environment| journal = IEEE Transactions on Neural Networks and Learning Systems| volume = 31| issue = 6| pages = 2064–2076| year = 2020| doi = 10.1109/TNNLS.2019.2930059| publisher = IEEE}}</ref>​​：提出自适应全卷积Q网络（AFCQN），结合局部地图信息，显著降低未知环境中的导航失败率。

​​​​​​*'''无人机控制​​'''：

​​​​​​

*'''无人机控制​​'''：

​​Kang等人（2019）<ref>{{cite journal|last1=Kang|first1=Kyunghoon|last2=Belkhale|first2=Suneel|last3=Kahn|first3=Gregory|last4=Abbeel|first4=Pieter|last5=Levine|first5=Sergey|title=Generalization through simulation: Integrating simulated and real data into deep reinforcement learning for vision-based autonomous flight|journal=IEEE International Conference on Robotics and Automation (ICRA)|year=2019|pages=6008-6014|doi=10.1109/ICRA.2019.8793701|publisher=IEEE}}​​</ref>：仅用单目视觉输入，训练四旋翼无人机在真实环境中实现动态避障。

​​Kang等人（2019）<ref>{{cite journal|last1=Kang|first1=Kyunghoon|last2=Belkhale|first2=Suneel|last3=Kahn|first3=Gregory|last4=Abbeel|first4=Pieter|last5=Levine|first5=Sergey|title=Generalization through simulation: Integrating simulated and real data into deep reinforcement learning for vision-based autonomous flight|journal=IEEE International Conference on Robotics and Automation (ICRA)|year=2019|pages=6008-6014|doi=10.1109/ICRA.2019.8793701|publisher=IEEE}}​​</ref>：仅用单目视觉输入，训练四旋翼无人机在真实环境中实现动态避障。