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Rather than programming, training allows robots to achieve behaviors that generalize better and are capable to respond to real-world needs.
Design and use paradigms for gazebo, an open-source multi-robot simulator
N. Koenig and A. Howard · 2004
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Bgp/mpls ip virtual private networks (vpns)
E. Rosen and Y. Rekhter · 2006
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Ros: an open-source robot operating system
M. Quigley, B. Gerkey, K. Conley, J. Faust, T. Foote, J. Leibs, E. Berger, R. Wheeler, and A. Ng · 2009
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Mujoco: A physics engine for model-based control
E. Todorov, T. Erez, and Y. Tassa · 2012
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Kubernetes and the path to cloud native
E. A. Brewer · 2015
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Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
S. Levine, P. Pastor, A. Krizhevsky, and D. Quillen · 2016
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Openai gym, 2016
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
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Extending the OpenAI Gym for robotics: a toolkit for reinforcement learning using ROS and Gazebo
I. Zamora, N. Gonzalez Lopez, V. Mayoral Vilches, and A. Hernandez Cordero · 2016
Cited alongside, same era.
Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
S. Gu, E. Holly, T. Lillicrap, and S. Levine · 2017
Cited alongside, same era.
Collective robot reinforcement learning with distributed asynchronous guided policy search
A. Yahya, A. Li, M. Kalakrishnan, Y. Chebotar, and S. Levine · 2017
Cited alongside, same era.
Ray RLlib: A Framework for Distributed Reinforcement Learning
E. Liang, R. Liaw, P. Moritz, R. Nishihara, R. Fox, K. Goldberg, J. E. Gonzalez, M. I. Jordan, and I. Stoica · 2017
Cited alongside, same era.
Reinforcement learning coach, Dec. 2017
I. Caspi, G. Leibovich, and G. Novik · 2017
Cited alongside, same era.
Openai baselines
Ray: A Distributed Framework for Emerging AI Applications
P. Moritz, R. Nishihara, S. Wang, A. Tumanov, R. Liaw, E. Liang, W. Paul, M. I. Jordan, and I. Stoica · 2017
Later among the works it cites.
The shift in the robotics paradigm: The hardware robot operating system (h-ros); an infrastructure to create interoperable robot components
V. Mayoral, A. Hernández, R. Kojcev, I. Muguruza, I. Zamalloa, A. Bilbao, and L. Usategi · 2017
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Proximal policy optimization algorithms
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov · 2017
Later among the works it cites.
Towards self-adaptable robots: from programming to training machines
V. Mayoral, R. Kojcev, N. Etxezarreta, A. Hernández, and I. Zamalloa · 2018
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Pytorch implementations of reinforcement learning algorithms
I. Kostrikov · 2018
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P. Dhariwal, C. Hesse, O. Klimov, A. Nichol, M. Plappert, A. Radford, J. Schulman, S. Sidor, and Y. Wu · 2017
Cited alongside, same era.
TensorFlow Agents: Efficient Batched Reinforcement Learning in TensorFlow
D. Hafner, J. Davidson, and V. Vanhoucke · 2017
Cited alongside, same era.
A. Stooke and P. Abbeel · 2018
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