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Planning problems in partially observable environments cannot be solved directly with convolutional networks and require some form of memory.
On the computational power of neural nets
Hava T Siegelmann and Eduardo D Sontag · 1995
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Richard S Sutton and Andrew G Barto · 1998
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Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard S Sutton, Doina Precup, and Satinder Singh · 1999
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Artificial Intelligence: A Modern Approach
Stuart J. Russell and Peter Norvig · 2003
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Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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Simultaneous localization and mapping
Sebastian Thrun and John J Leonard · 2008
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Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Human-friendly robot navigation in dynamic environments
Jérôme Guzzi, Alessandro Giusti, Luca M Gambardella, Guy Theraulaz, and Gianni A Di Caro · 2013
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Exploring deep and recurrent architectures for optimal control
Sergey Levine · 2013
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Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
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Value iteration networks
Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, and Pieter Abbeel · 2016
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Learning deep neural network policies with continuous memory states
Marvin Zhang, Zoe McCarthy, Chelsea Finn, Sergey Levine, and Pieter Abbeel · 2016
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Learning deep control policies for autonomous aerial vehicles with mpc-guided policy search
Tianhao Zhang, Gregory Kahn, Sergey Levine, and Pieter Abbeel · 2016
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Neural network memory architectures for autonomous robot navigation
Steven W Chen, Nikolay Atanasov, Arbaaz Khan, Konstantinos Karydis, Daniel D Lee, and Vijay Kumar · 2017
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Cognitive mapping and planning for visual navigation
Saurabh Gupta, James Davidson, Sergey Levine, Rahul Sukthankar, and Jitendra Malik · 2017
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Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin Riedmiller
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