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Learn-to-Race Autonomous Racing Virtual Challenge hosted on www<dot>aicrowd<dot>com platform consisted of two tracks: Single and Multi Camera.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
Earlier work this paper cites.
Continuous control with deep reinforcement learning
Lillicrap, T., Hunt, J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D · 2015
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
Earlier work this paper cites.
Openai gym, 2016
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W · 2016
Earlier work this paper cites.
An approach to hierarchical deep reinforcement learning for a decentralized walking control architecture
Schilling, M. and Melnik, A · 2018
Earlier work this paper cites.
Biologically-inspired deep reinforcement learning of modular control for a six-legged robot
Konen, K., Korthals, T., Melnik, A., and Schilling, M · 2019
Earlier work this paper cites.
Modularization of end-to-end learning: Case study in arcade games
Melnik, A., Fleer, S., Schilling, M., and Ritter, H · 2019
Cited alongside, same era.
End-to-end autonomous driving controller using semantic segmentation and variational autoencoder
Azizpour, M., da Roza, F., and Bajcinca, N · 2020
Cited alongside, same era.
Solving physics puzzles by reasoning about paths
Harter, A., Melnik, A., Kumar, G., Agarwal, D., Garg, A., and Ritter, H · 2020
Cited alongside, same era.
Learn-to-race autonomous racing virtual challenge, 2022a
AICrowd · 2021
Cited alongside, same era.
Learn-to-race: A multimodal control environment for autonomous racing
Herman, J., Francis, J., Ganju, S., Chen, B., Koul, A., Gupta, A., Skabelkin, A., Zhukov, I., Kumskoy, M., and Nyberg, E · 2021
Cited alongside, same era.
Critic guided segmentation of rewarding objects in first-person views
Melnik, A., Harter, A., Limberg, C., Rana, K., Sünderhauf, N., and Ritter, H · 2021
Later among the works it cites.
Decentralized control and local information for robust and adaptive decentralized deep reinforcement learning
Schilling, M., Melnik, A., Ohl, F. W., Ritter, H. J., and Hammer, B · 2021
Later among the works it cites.
Learn-to-race uniteam solution, 2022
Beohar, S · 2022
Closest in time.
Planning with rl and episodic-memory behavioral priors
Beohar, S. and Melnik, A · 2022
Closest in time.
Albumentations: Fast and flexible image augmentations
Buslaev, A., Iglovikov, V. I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A. A · 2078
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Learn-to-race: Autonomous racing virtual challenge announcement. congratulations to the winners!, 2022b
AICrowd
Cited in the paper.
An error-based addressing architecture for dynamic model learning
Bach, N., Melnik, A., Rosetto, F., and Ritter, H
Cited in the paper.
Learn to move through a combination of policy gradient algorithms: Ddpg, d4pg, and td3
Bach, N., Melnik, A., Schilling, M., Korthals, T., and Ritter, H
Cited in the paper.