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This paper presents research in progress investigating the viability and adaptation of reinforcement learning using deep neural network based function approximation for the task of radio control and signal detection in the wireless domain.
“Reinforcement learning: An introduction”
Richard˜S Sutton and Andrew˜G Barto · 1998
Earlier work this paper cites.
“Theano: a CPU and GPU Math Expression Compiler” Oral Presentation
James Bergstra et al · 2010
Earlier work this paper cites.
“Playing atari with deep reinforcement learning”
Volodymyr Mnih et al · 2013
Earlier work this paper cites.
“Adam: A method for stochastic optimization”
Diederik Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
“Deep recurrent q-learning for partially observable mdps”
Matthew Hausknecht and Peter Stone · 2015
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“Deep reinforcement learning with double Q-learning”
Hado Van˜Hasselt, Arthur Guez and David Silver · 2015
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“TensorFlow: Large-scale machine learning on heterogeneous systems, 2015”
Martn Abadi et al · 2015
Cited alongside, same era.
“Benchmarking Deep Reinforcement Learning for Continuous Control”
Yan Duan et al · 2016
Cited alongside, same era.
“KeRLym: KEras Reinforcement Learning gYM agents”
Tim O’Shea · 2016
Closest in time.
“Convolutional Radio Modulation Recognition Networks”
Timothy˜J O’Shea, Johnathan Corgan and T.˜Charles Clancy · 2016
Closest in time.
“Mastering the game of Go with deep neural networks and tree search”
David Silver et al · 2016
Closest in time.
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