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We propose to deal with sequential processes where only partial observations are available by learning a latent representation space on which policies may be accurately learned.
An on-line algorithm for dynamic reinforcement learning and planning in reactive environments
Schmidhuber, Jürgen · 1990
Earlier work this paper cites.
Reinforcement learning: An introduction , volume 1
Sutton, Richard S and Barto, Andrew G · 1998
Earlier work this paper cites.
Rollout sampling approximate policy iteration
Dimitrakakis, Christos and Lagoudakis, Michail G · 2008
Earlier work this paper cites.
Learning deep architectures for ai
Bengio, Yoshua · 2009
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Intrinsically motivated neuroevolution for vision-based reinforcement learning
Cuccu, Giuseppe, Luciw, Matthew, Schmidhuber, Jürgen, and Gomez, Faustino · 2011
Cited alongside, same era.
Sequential constant size compressors for reinforcement learning
Gisslén, Linus, Luciw, Matt, Graziano, Vincent, and Schmidhuber, Jürgen · 2011
Cited alongside, same era.
Solving partially observable reinforcement learning problems with recurrent neural networks
Duell, Siegmund, Udluft, Steffen, and Sterzing, Volkmar · 2012
Later among the works it cites.
Unsupervised model-free representation learning
Ryabko, Daniil · 2013
Closest in time.
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