# End-to-End Deep Reinforcement Learning for Image-Based UAV Autonomous Control
## 4 Proposed Method
### Algorithm Implementation
By virtue of the powerful feature representation ability of the deep neural network, the input image can be directly interpreted to generate continuous actuator control commands. In addition, the model-free reinforcement learning algorithm allows the agent to learn the control policy without prior information about the dynamics. We believe that the idea of end-to-end control can be applied in other missions, and that it has the potential to simplify the deployment of cascade controllers in flight experiments with proper adjustment. For future work, other neural network architectures and learning algorithms will be explored in order to improve the performance. Then, flight experiments will be conducted regarding the training results in the simulation, deploying part of or the whole end-to-end network in the autonomous control of a UAV in a specific scenario. In addition, a further comparison between the proposed method with the traditional method will be performed. Additionally, we expect that the proposed end-to-end autonomous control method can be used in a variety of missions other than landing, in which the constraints need to be considered with respect to practical problems and the reward function and terminal state criterion should be reasonably determined. Conceptualization, J.Z. and Z.C. ; Methodology, J.Z., J.S. and L.W. ; Project administration, J.Z. ; Resources, Z.C. and Y.W. ; Writing--original draft, J.Z. and J.S. ; Writing--review & editing, J.S. All authors have read and agreed to the published version of the manuscript. This research was funded by the Aeronautical Science Foundation of China (No. 20175851032) and Fundamental Research Funds for the Central Universities of China (No.YWF-21-BJ-541). Not applicable. Figure 9:8 The control commands for the actuator under case 3. ## References

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