Built on

Sutton, R.S., Barto, A.G.: Reinforcement learning - an introduction. Adaptive computation and machine learning, MIT Press (1998), http://www.worldcat.org/oclc/37293240
1998
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Guennebaud, G., Jacob, B., et al.: Eigen v3. http://eigen.tuxfamily.org (2010)
2010
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Niu, F., Recht, B., Re, C., Wright, S.J.: HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent. ArXiv e-prints (Jun 2011)
2011
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Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(1), 281–305 (Feb 2012), http://dl.acm.org/citation.cfm?id=2503308.2188395
2012
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Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R.B., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. CoRR abs/1408.5093 (2014), http://arxiv.org/abs/1408.5093
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2014
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Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. CoRR abs/1412.6980 (2014), http://arxiv.org/abs/1412.6980
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2014
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Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015), http://tensorflow.org/ , software available from tensorflow.org
2015
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Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015), http://dx.doi.org/10.1038/nature14236
2015
Earlier work this paper cites.

Similar

Nair, A., Srinivasan, P., Blackwell, S., Alcicek, C., Fearon, R., Maria, A.D., Panneershelvam, V., Suleyman, M., Beattie, C., Petersen, S., Legg, S., Mnih, V., Kavukcuoglu, K., Silver, D.: Massively parallel methods for deep reinforcement learning. CoRR abs/1507.04296 (2015), http://arxiv.org/abs/1507.04296
Original
2015
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Babaeizadeh, M., Frosio, I., Tyree, S., Clemons, J., Kautz, J.: GA3C: gpu-based A3C for deep reinforcement learning. CoRR abs/1611.06256 (2016), http://arxiv.org/abs/1611.06256
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Original
2016

Then

Wu, Y.: Tensorpack. https://github.com/ppwwyyxx/tensorpack (2016)
Original
2016
Later among the works it cites.
Caffe optimized for Intel architecture: Applying modern code techniques (Feb 2017), https://software.intel.com/en-us/articles/caffe-optimized-for-intel-architecture-applying-modern-code-techniques
2017
Closest in time.
FALCON Library: Fast Image Convolution in Neural Networks on Intel architecture (Feb 2017), https://colfaxresearch.com/falcon-library/
2017
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Mulnix, D.: Intel xeon processor e5-2600 v4 product family technical overview (Jan 2017), https://software.intel.com/en-us/articles/intel-xeon-processor-e5-2600-v4-product-family-technical-overview
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Pirogov, V.: Introducing DNN primitives in Intel Math Kernel Library (Mar 2017), https://software.intel.com/en-us/articles/introducing-dnn-primitives-in-intelr-mkl

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2017

Atari games and Intel processors

Adamski, Robert, Grel, Tomasz, Klimek, Maciej et al.

Understand

The asynchronous nature of the state-of-the-art reinforcement learning algorithms such as the Asynchronous Advantage Actor-Critic algorithm, makes them exceptionally suitable for CPU computations.

However, given the fact that deep reinforcement learning often deals with interpreting visual information, a large part of the train and inference time is spent performing convolutions.
In this work we present our results on learning strategies in Atari games using a Convolutional Neural Network, the Math Kernel Library and TensorFlow 0.11rc0 machine learning framework.
We also analyze effects of asynchronous computations on the convergence of reinforcement learning algorithms.

Built on

Sutton, R.S., Barto, A.G.: Reinforcement learning - an introduction. Adaptive computation and machine learning, MIT Press (1998), http://www.worldcat.org/oclc/37293240
1998
Earlier work this paper cites.
Guennebaud, G., Jacob, B., et al.: Eigen v3. http://eigen.tuxfamily.org (2010)
2010
Earlier work this paper cites.
Niu, F., Recht, B., Re, C., Wright, S.J.: HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent. ArXiv e-prints (Jun 2011)
2011
Earlier work this paper cites.
Bergstra, J., Bengio, Y.: Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13(1), 281–305 (Feb 2012), http://dl.acm.org/citation.cfm?id=2503308.2188395
2012
Earlier work this paper cites.
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R.B., Guadarrama, S., Darrell, T.: Caffe: Convolutional architecture for fast feature embedding. CoRR abs/1408.5093 (2014), http://arxiv.org/abs/1408.5093
Original
2014
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. CoRR abs/1412.6980 (2014), http://arxiv.org/abs/1412.6980
Original
2014
Earlier work this paper cites.
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viégas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., Zheng, X.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015), http://tensorflow.org/ , software available from tensorflow.org
2015
Earlier work this paper cites.
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M.A., Fidjeland, A., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., Hassabis, D.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015), http://dx.doi.org/10.1038/nature14236
2015
Earlier work this paper cites.

Similar

Nair, A., Srinivasan, P., Blackwell, S., Alcicek, C., Fearon, R., Maria, A.D., Panneershelvam, V., Suleyman, M., Beattie, C., Petersen, S., Legg, S., Mnih, V., Kavukcuoglu, K., Silver, D.: Massively parallel methods for deep reinforcement learning. CoRR abs/1507.04296 (2015), http://arxiv.org/abs/1507.04296
Original
2015
Cited alongside, same era.
Intel Xeon Phi delivers competitive performance for deep learning—and getting better fast (Dec 2016), https://software.intel.com/en-us/articles/intel-xeon-phi-delivers-competitive-performance-for-deep-learning-and-getting-better-fast
2016
Cited alongside, same era.
Babaeizadeh, M., Frosio, I., Tyree, S., Clemons, J., Kautz, J.: GA3C: gpu-based A3C for deep reinforcement learning. CoRR abs/1611.06256 (2016), http://arxiv.org/abs/1611.06256
Original
2016
Cited alongside, same era.
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., Zaremba, W.: Openai gym. CoRR abs/1606.01540 (2016), http://arxiv.org/abs/1606.01540
Original
2016

Then

Wu, Y.: Tensorpack. https://github.com/ppwwyyxx/tensorpack (2016)
Original
2016
Later among the works it cites.
Caffe optimized for Intel architecture: Applying modern code techniques (Feb 2017), https://software.intel.com/en-us/articles/caffe-optimized-for-intel-architecture-applying-modern-code-techniques
2017
Closest in time.
FALCON Library: Fast Image Convolution in Neural Networks on Intel architecture (Feb 2017), https://colfaxresearch.com/falcon-library/
2017
Closest in time.
Mulnix, D.: Intel xeon processor e5-2600 v4 product family technical overview (Jan 2017), https://software.intel.com/en-us/articles/intel-xeon-processor-e5-2600-v4-product-family-technical-overview
2017
Closest in time.
Pirogov, V.: Introducing DNN primitives in Intel Math Kernel Library (Mar 2017), https://software.intel.com/en-us/articles/introducing-dnn-primitives-in-intelr-mkl

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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Cited alongside, same era.

Duan, Y., Chen, X., Houthooft, R., Schulman, J., Abbeel, P.: Benchmarking deep reinforcement learning for continuous control. CoRR abs/1604.06778 (2016), http://arxiv.org/abs/1604.06778

Original

2016

Cited alongside, same era.

Dubey, P.: Myth busted: General purpose CPUs can’t tackle deep neural network training (Jun 2016), https://itpeernetwork.intel.com/myth-busted-general-purpose-cpus-cant-tackle-deep-neural-network-training/

2016

Cited alongside, same era.

Mnih, V., Badia, A.P., Mirza, M., Graves, A., Lillicrap, T.P., Harley, T., Silver, D., Kavukcuoglu, K.: Asynchronous methods for deep reinforcement learning. CoRR abs/1602.01783 (2016), http://arxiv.org/abs/1602.01783

Original

2016

Cited alongside, same era.

Mark O’Connor: Deep Learning Episode 4: Supercomputer vs Pong II (Oct 2016), https://www.allinea.com/blog/201610/deep-learning-episode-4-supercomputer-vs-pong-ii

2016

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2017

Closest in time.

Salimans, T., Ho, J., Chen, X., Sutskever, I.: Evolution strategies as a scalable alternative to reinforcement learning (Mar 2017), https://arxiv.org/abs/1703.03864

Original

2017

Closest in time.

Ould-ahmed vall, E.: Optimizing Tensorflow on Intel architecture for AI applications (Mar 2017), https://itpeernetwork.intel.com/tensorflow-intel-architecture-ai/

2017

Closest in time.