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The most data-efficient algorithms for reinforcement learning in robotics are model-based policy search algorithms, which alternate between learning a dynamical model of the robot and optimizing a policy to maximize the expected return given the model and its uncertainties.
Simple genetic algorithms and the minimal, deceptive problem
David E Goldberg · 1987
Earlier work this paper cites.
A Possibility for Implementing Curiosity and Boredom in Model-building Neural Controllers
Jürgen Schmidhuber · 1990
Earlier work this paper cites.
Rprop-a fast adaptive learning algorithm
Martin Riedmiller and Heinrich Braun · 1992
Earlier work this paper cites.
Reinforcement learning: A survey
Leslie Pack Kaelbling, Michael L Littman, and Andrew W Moore · 1996
Earlier work this paper cites.
Self-adaptive genetic algorithms with simulated binary crossover
Kalyanmoy Deb and Hans-Georg Beyer · 1999
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Random forests
Leo Breiman · 2001
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Earlier work this paper cites.
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Earlier work this paper cites.
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Pierre-Yves Oudeyer, Frdric Kaplan, and Verena V Hafner · 2007
Earlier work this paper cites.
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Jean-Baptiste Mouret and Stéphane Doncieux · 2010
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Earlier work this paper cites.
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Marcin Andrychowicz et al · 2017
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