Learning Embodied Agents with Policy Gradients to Navigate in Realistic Environments

Author:

Staroverov Alexey,Vetlin Vladislav,Makarenko Stepan,Naumov Anton,Panov Aleksandr I.

Publisher

Springer International Publishing

Reference12 articles.

1. Abhishek Kadian*, Joanne Truong*, Gokaslan, A., Clegg, A., Wijmans, E., Lee, S., Savva, M., Chernova, S., Batra, D.: Are we making real progress in simulated environments? measuring the sim2real gap in embodied visual navigation. arXiv preprint arXiv:1912.06321 (2019)

2. Chaplot, D.S., Gandhi, D., Gupta, S., Gupta, A., Salakhutdinov, R.: Learning to explore using active neural slam (2020)

3. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

4. Hui, T.W., Tang, X., Loy, C.C.: LiteFlowNet: a lightweight convolutional neural network for optical flow estimation (2018)

5. Labbé, M., Michaud, F.: RTAB-Map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation. J. Field Robot. 36(2), 416–446 (2019). https://doi.org/10.1002/rob.21831

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