| Reference: | [1] Gebhardt, C., Stevšić, S., & Hilliges, O. (2018). Optimizing for aesthetically
pleasing quadrotor camera motion. ACM Transactions on Graphics (TOG), 37(4),
1-11.
[2] Hepp, B., Dey, D., Sinha, S. N., Kapoor, A., Joshi, N., & Hilliges, O. (2018).
Learn-to-score: Efficient 3d scene exploration by predicting view utility. In
Proceedings of the European conference on computer vision (ECCV) (pp. 437-
452).
[3] Kaufmann, E., Loquercio, A., Ranftl, R., Dosovitskiy, A., Koltun, V., &
Scaramuzza, D. (2018, October). Deep drone racing: Learning agile flight in
dynamic environments. In Conference on Robot Learning (pp. 133-145). PMLR.
[4] Xu, J., Du, T., Foshey, M., Li, B., Zhu, B., Schulz, A., & Matusik, W. (2019).
Learning to fly: computational controller design for hybrid uavs with
reinforcement learning. ACM Transactions on Graphics (TOG), 38(4), 1-12.
[5] Shin, S. Y., Kang, Y. W., & Kim, Y. G. (2020). Reward-driven U-net training for
obstacle avoidance drone. Expert Systems with Applications, 143, 113064.
[6] Shin, S. Y., Kang, Y. W., & Kim, Y. G. (2019). Obstacle avoidance drone by deep
reinforcement learning and its racing with human pilot. Applied sciences, 9(24),
5571.
[7] Madaan, R., Gyde, N., Vemprala, S., Brown, M., Nagami, K., Taubner, T., ... &
Kapoor, A. (2020, August). Airsim drone racing lab. In NeurIPS 2019 Competition
and Demonstration Track (pp. 177-191). PMLR.
[8] Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., &
Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv
preprint arXiv:13
[9] Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., ... & Wierstra,
D. (2015). Continuous control with deep reinforcement learning. arXiv preprint
arXiv:1509.02971.
[10] Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., ... &
Kavukcuoglu, K. (2016, June). Asynchronous methods for deep reinforcement
learning. In International conference on machine learning (pp. 1928-1937).
PMLR.
[11] Wu, Y., Mansimov, E., Grosse, R. B., Liao, S., & Ba, J. (2017). Scalable trustregion method for deep reinforcement learning using kronecker-factored
approximation. Advances in neural information processing systems, 30, 5279-
5288.
[12] Won, J., Park, J., Kim, K., & Lee, J. (2017). How to train your dragon: exampleguided control of flapping flight. ACM Transactions on Graphics (TOG), 36(6), 1-
13.
[13] Simonyan, K., Vedaldi, A., & Zisserman, A. (2013). Deep inside convolutional
networks: Visualising image classification models and saliency maps. arXiv
preprint arXiv:1312.6034.
[14] Iyer, R., Li, Y., Li, H., Lewis, M., Sundar, R., & Sycara, K. (2018, December).
Transparency and explanation in deep reinforcement learning neural networks. In
Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society (pp.
144-150).
[15] Wang, X., Li, H., Zhang, H., Lewis, M., & Sycara, K. (2020). Explanation of
Reinforcement Learning Model in Dynamic Multi-Agent System. arXiv preprint
arXiv:2008.01508.
[16] Deshpande, S., Eysenbach, B., & Schneider, J. (2020). Interactive Visualization
for Debugging RL. arXiv preprint arXiv:2008.07331.
[17] Greydanus, S., Koul, A., Dodge, J., & Fern, A. (2018, July). Visualizing and
understanding atari agents. In International Conference on Machine Learning (pp.
1792-1801). PMLR.
[18] Dabkowski, P., & Gal, Y. (2017). Real time image saliency for black box
classifiers. arXiv preprint arXiv:1705.07857.
[19] Fong, R. C., & Vedaldi, A. (2017). Interpretable explanations of black boxes by
meaningful perturbation. In Proceedings of the IEEE international conference on
computer vision (pp. 3429-3437).
[20] Rosynski, M., Kirchner, F., & Valdenegro-Toro, M. (2020). Are Gradient-based
Saliency Maps Useful in Deep Reinforcement Learning?. arXiv preprint
arXiv:2012.01281.
[21] Atrey, A., Clary, K., & Jensen, D. (2019). Exploratory not explanatory:
Counterfactual analysis of saliency maps for deep reinforcement learning. arXiv
preprint arXiv:1912.05743.
[22] Wang, J., Gou, L., Shen, H. W., & Yang, H. (2018). Dqnviz: A visual analytics
approach to understand deep q-networks. IEEE transactions on visualization and
computer graphics, 25(1), 288-298.
[23] Jaunet, T., Vuillemot, R., & Wolf, C. (2020, June). DRLViz: Understanding
decisions and memory in deep reinforcement learning. In Computer Graphics
Forum (Vol. 39, No. 3, pp. 49-61).
[24] Jaderberg, M., Czarnecki, W. M., Dunning, I., Marris, L., Lever, G., Castaneda, A.
G., ... & Graepel, T. (2019). Human-level performance in 3D multiplayer games
with population-based reinforcement learning. Science, 364(6443), 859-865.
[25] Deng, Z., Weng, D., Chen, J., Liu, R., Wang, Z., Bao, J., ... & Wu, Y. (2019). Airvis:
Visual analytics of air pollution propagation. IEEE transactions on visualization
and computer graphics, 26(1), 800-810.
[26] Ates, U. (2020, October). Long-Term Planning with Deep Reinforcement
Learning on Autonomous Drones. In 2020 Innovations in Intelligent Systems and
Applications Conference (ASYU) (pp. 1-6). IEEE.
[27] Chattopadhay, A., Sarkar, A., Howlader, P., & Balasubramanian, V. N. (2018,
March). Grad-cam++: Generalized gradient-based visual explanations for deep
convolutional networks. In 2018 IEEE winter conference on applications of
computer vision (WACV) (pp. 839-847). IEEE.
[28] Mott, A., Zoran, D., Chrzanowski, M., Wierstra, D., & Rezende, D. J. (2019).
Towards interpretable reinforcement learning using attention augmented agents.
arXiv preprint arXiv:1906.02500.
[29] Puri, N., Verma, S., Gupta, P., Kayastha, D., Deshmukh, S., Krishnamurthy, B., &
Singh, S. (2019). Explain your move: Understanding agent actions using specific
and relevant feature attribution. arXiv preprint arXiv:1912.12191.
[30] Kostrikov, I.. (2018). PyTorch Implementations of Reinforcement Learning
Algorithms. https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail.
[31] Tzutalin. LabelImg. Git code (2015). https://github.com/tzutalin/labelImg
[32] Quigley, M., Conley, K., Gerkey, B., Faust, J., Foote, T., Leibs, J., ... & Ng, A. Y.
(2009, May). ROS: an open-source Robot Operating System. In ICRA workshop
on open source software (Vol. 3, No. 3.2, p. 5).
[33] Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., &
Zaremba, W. (2016). Openai gym. arXiv preprint arXiv:1606.01540.
[34] Amdegroot. (2017). SSD.PyTorch. https://github.com/amdegroot/ssd.pytorch
[35] Reinforcement learning basic architecture diagram https://www.newton.com.tw/wiki
[36] Actor critic architecture http://incompleteideas.net/book/ebook |
