Continuous control with deep reinforcement learning

  title={Continuous control with deep reinforcement learning},
  author={Timothy P. Lillicrap and Jonathan J. Hunt and Alexander Pritzel and Nicolas Heess and Tom Erez and Yuval Tassa and David Silver and Daan Wierstra},
We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain. We present an actor-critic, model-free algorithm based on the deterministic policy gradient that can operate over continuous action spaces. Using the same learning algorithm, network architecture and hyper-parameters, our algorithm robustly solves more than 20 simulated physics tasks, including classic problems such as cartpole swing-up, dexterous manipulation, legged locomotion and car driving. Our… CONTINUE READING
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Publications referenced by this paper.
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Humanlevel control through deep reinforcement learning

Mnih, Volodymyr, +17 authors Georg
Nature, 518(7540):529–533, • 2015
View 16 Excerpts
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arXiv preprint arXiv:1504.00702, • 2015
View 3 Excerpts
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View 2 Excerpts
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International Journal of Machine Learning and Computing, • 2015

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