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Transferring learning-based models to the real world remains one of the hardest problems in model-free control theory.
A new potential-based reward shaping for reinforcement learning agent
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Policy gradient methods for reinforcement learning with function approximation
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Path following mobile robot in the presence of velocity constraints
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Design and use paradigms for Gazebo, an open-source multi-robot simulator
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Theory and application of reward shaping in reinforcement learning
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Curriculum learning
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Octomap: A probabilistic, flexible, and compact 3d map representation for robotic systems
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Gpgpu processing in cuda architecture
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The Open Motion Planning Library
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Rectifier nonlinearities improve neural network acoustic models
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Playing Atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013) · 2013
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Chiang, H.-T. L., Faust, A., Fiser, M., and Francis, A. (2019) · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
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Zaremba, W. and Sutskever, I. (2014) · 2014
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Sim-to-real robot learning from pixels with progressive nets
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Evolution strategies as a scalable alternative to reinforcement learning
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Mastering the game of go without human knowledge
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Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation
Tai, L., Paolo, G., and Liu, M. (2017) · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
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Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, D. (2015) · 2015
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Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I. D., and Leonard, J. J. (2016) · 2016
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Kamel, M., Stastny, T., Alexis, K., and Siegwart, R. (2017) · 2017
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Towards monocular vision based obstacle avoidance through deep reinforcement learning
Xie, L., Wang, S., Markham, A., and Trigoni, N. (2017) · 2017
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S. (2018) · 2018
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Explainable reinforcement learning via reward decomposition
Juozapaitis, Z., Koul, A., Fern, A., Erwig, M., and Doshi-Velez, F. (2019) · 2019
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Meta-sim: Learning to generate synthetic datasets
Kar, A., Prakash, A., Liu, M.-Y., Cameracci, E., Yuan, J., Rusiniak, M., Acuna, D., Torralba, A., and Fidler, S. (2019) · 2019
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Vision-based navigation using deep reinforcement learning
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S. (2019) · 2019
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Learning to simulate
Ruiz, N., Schulter, S., and Chandraker, M. (2019) · 2019
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A fully-autonomous aerial robot for search and rescue applications in indoor environments using learning-based techniques
Sampedro, C., Rodriguez-Ramos, A., Bavle, H., Carrio, A., de la Puente, P., and Campoy, P. (2019) · 2019
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