• Corpus ID: 6875312

Asynchronous Methods for Deep Reinforcement Learning

@inproceedings{Mnih2016AsynchronousMF,
  title={Asynchronous Methods for Deep Reinforcement Learning},
  author={Volodymyr Mnih and Adri{\`a} Puigdom{\`e}nech Badia and Mehdi Mirza and Alex Graves and Timothy P. Lillicrap and Tim Harley and David Silver and Koray Kavukcuoglu},
  booktitle={ICML},
  year={2016}
}
We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers. [] Key Method The best performing method, an asynchronous variant of actor-critic, surpasses the current state-of-the-art on the Atari domain while training for half the time on a single multi-core CPU instead of a GPU. Furthermore, we show that asynchronous actor-critic succeeds on a wide variety of continuous motor control…

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