Fetching the paper…
Reading the bibliography…
We present an approach for agents to learn representations of a global map from sensor data, to aid their exploration in new environments.
The hippocampus as a cognitive map
John O’keefe and Lynn Nadel · 1978
Earlier work this paper cites.
Robot Motion Planning
J.C. Latombe · 1991
Earlier work this paper cites.
Animal navigation: path integration, visual landmarks and cognitive maps
Thomas S Collett and Paul Graham · 2004
Earlier work this paper cites.
Design and use paradigms for gazebo, an open-source multi-robot simulator
Nathan Koenig and Andrew Howard · 2004
Earlier work this paper cites.
Probabilistic Robotics
S. Thrun, W. Burgard, and D. Fox · 2005
Earlier work this paper cites.
Planning Algorithms
S.M. LaValle · 2006
Earlier work this paper cites.
Path integration and the neural basis of the ’cognitive map’
Bruce L McNaughton, Francesco P Battaglia, Ole Jensen, Edvard I Moser, and May-Britt Moser · 2006
Earlier work this paper cites.
How can we define intrinsic motivation?
Pierre-Yves Oudeyer and Frederic Kaplan · 2008
Earlier work this paper cites.
Robotic mapping and exploration , volume 55
Cyrill Stachniss · 2009
Earlier work this paper cites.
Formal theory of creativity, fun, and intrinsic motivation (1990–2010)
Jürgen Schmidhuber · 2010
Earlier work this paper cites.
Hogwild: A lock-free approach to parallelizing stochastic gradient descent
Benjamin Recht, Christopher Re, Stephen Wright, and Feng Niu · 2011
Cited alongside, same era.
Autonomous indoor 3d exploration with a micro-aerial vehicle
Shaojie Shen, Nathan Michael, and Vijay Kumar · 2012
Cited alongside, same era.
Deep learning of representations: Looking forward
Yoshua Bengio · 2013
Cited alongside, same era.
Alex Graves, Greg Wayne, and Ivo Danihelka · 2014
Cited alongside, same era.
Flownet: Learning optical flow with convolutional networks
Philipp Fischer, Alexey Dosovitskiy, Eddy Ilg, Philip Häusser, Caner Hazırbaş, Vladimir Golkov, Patrick van der Smagt, Daniel Cremers, and Thomas Brox · 2015
Cited alongside, same era.
Learning to navigate in complex environments
Piotr Mirowski, Razvan Pascanu, Fabio Viola, Hubert Soyer, Andy Ballard, Andrea Banino, Misha Denil, Ross Goroshin, Laurent Sifre, Koray Kavukcuoglu, et al · 2016
Later among the works it cites.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adria Puigdomenech Badia, Mehdi Mirza, Alex Graves, Timothy Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu · 2016
Later among the works it cites.
Control of memory, active perception, and action in minecraft
Junhyuk Oh, Valliappa Chockalingam, Satinder Singh, and Honglak Lee · 2016
Later among the works it cites.
Value iteration networks
Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, and Pieter Abbeel · 2016
Later among the works it cites.
Deep reinforcement learning with successor features for navigation across similar environments
Jingwei Zhang, Jost Tobias Springenberg, Joschka Boedecker, and Wolfram Burgard · 2016
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
Cited alongside, same era.
High-dimensional continuous control using generalized advantage estimation
John Schulman, Philipp Moritz, Sergey Levine, Michael Jordan, and Pieter Abbeel · 2015
Cited alongside, same era.
End-to-end memory networks
Sainbayar Sukhbaatar, Jason Weston, Rob Fergus, et al · 2015
Cited alongside, same era.
Playing doom with slam-augmented deep reinforcement learning
Shehroze Bhatti, Alban Desmaison, Ondrej Miksik, Nantas Nardelli, N Siddharth, and Philip HS Torr · 2016
Cited alongside, same era.
Hybrid computing using a neural network with dynamic external memory
Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwińska, Sergio Gómez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John Agapiou, et al · 2016
Cited alongside, same era.
Cognitive mapping and planning for visual navigation
Saurabh Gupta, James Davidson, Sergey Levine, Rahul Sukthankar, and Jitendra Malik
Cited in the paper.
Unifying map and landmark based representations for visual navigation
Saurabh Gupta, David Fouhey, Sergey Levine, and Jitendra Malik
Cited in the paper.
Later among the works it cites.
Target-driven visual navigation in indoor scenes using deep reinforcement learning
Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J Lim, Abhinav Gupta, Li Fei-Fei, and Ali Farhadi · 2016
Later among the works it cites.
Training recurrent networks to generate hypotheses about how the brain solves hard navigation problems
Ingmar Kanitscheider and Ila Fiete · 2017
Closest in time.
Neural map: Structured memory for deep reinforcement learning
Emilio Parisotto and Ruslan Salakhutdinov · 2017
Closest in time.
Lei Tai, Giuseppe Paolo, and Ming Liu · 2017
Closest in time.
Devendra Singh Chaplot, Emilio Parisotto, and Ruslan Salakhutdinov · 2018
Closest in time.