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Multisensory polices are known to enhance both state estimation and target tracking.
Policy gradient methods for reinforcement learning with function approximation
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Torcs, the open racing car simulator
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Tartan racing: A multi-modal approach to the darpa urban challenge
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A reduction of imitation learning and structured prediction to no-regret online learning
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Multimodal deep learning
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Multimodal learning with deep boltzmann machines
N. Srivastava and R. R. Salakhutdinov · 2012
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Playing atari with deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller · 2013
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Regularization of neural networks using dropconnect
L. Wan, M. Zeiler, S. Zhang, Y. L. Cun, and R. Fergus · 2013
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A multi-sensor fusion system for moving object detection and tracking in urban driving environments
H. Cho, Y.-W. Seo, B. V. Kumar, and R. R. Rajkumar · 2014
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Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Dropall: Generalization of two convolutional neural network regularization methods
X. Frazão and L. A. Alexandre · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Deterministic policy gradient algorithms
D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra, and M. Riedmiller · 2014
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Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al · 2015
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Trust region policy optimization
J. Schulman, S. Levine, P. Abbeel, M. I. Jordan, and P. Moritz · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. L. Ba · 2015
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Zoneout: Regularizing rnns by randomly preserving hidden activations
D. Krueger, T. Maharaj, J. Kramár, M. Pezeshki, N. Ballas, N. R. Ke, A. Goyal, Y. Bengio, H. Larochelle, A. Courville, et al · 2016
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Moddrop: adaptive multi-modal gesture recognition
N. Neverova, C. Wolf, G. Taylor, and F. Nebout · 2016
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Asynchronous methods for deep reinforcement learning
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016
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Reinforcement learning with unsupervised auxiliary tasks
M. Jaderberg, V. Mnih, W. M. Czarnecki, T. Schaul, J. Z. Leibo, D. Silver, and K. Kavukcuoglu · 2016
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Gym-torcs
N. Yoshida · 2016
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M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, et al · 2016
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Continuous deep q-learning with model-based acceleration
S. Gu, T. Lillicrap, I. Sutskever, and S. Levine · 2016
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Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2016
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Robot gains social intelligence through multimodal deep reinforcement learning
A. H. Qureshi, Y. Nakamura, Y. Yoshikawa, and H. Ishiguro · 2016
Cited alongside, same era.
End-to-end training of deep visuomotor policies
S. Levine, C. Finn, T. Darrell, and P. Abbeel · 2016
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Blockout: Dynamic model selection for hierarchical deep networks
C. Murdock, Z. Li, H. Zhou, and T. Duerig · 2016
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Composing meta-policies for autonomous driving using hierarchical deep reinforcement learning
R. Liaw, S. Krishnan, A. Garg, D. Crankshaw, J. E. Gonzalez, and K. Goldberg
Cited in the paper.
Dueling network architectures for deep reinforcement learning
Z. Wang, N. de Freitas, and M. Lanctot · 2016
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Using keras and deep deterministic policy gradient to play torcs
Y.-P. Lau · 2016
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Sensor modality fusion with cnns for ugv autonomous driving in indoor environments
N. Patel, A. Choromanska, P. Krishnamurthy, and F. Khorrami · 2017
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Sensor fusion for robot control through deep reinforcement learning
S. Bohez, T. Verbelen, E. De Coninck, B. Vankeirsbilck, P. Simoens, and B. Dhoedt · 2017
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Learning to navigate in complex environments
P. Mirowski, R. Pascanu, F. Viola, H. Soyer, A. Ballard, A. Banino, M. Denil, R. Goroshin, L. Sifre, K. Kavukcuoglu, D. Kumaran, and R. Hadsell · 2017
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Q-prop: Sample-efficient policy gradient with an off-policy critic
S. Gu, T. P. Lillicrap, Z. Ghahramani, R. E. Turner, and S. Levine · 2017
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