Distributed Deep Reinforcement Learning: Learn how to play Atari games in 21 minutes

Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res. 12, 2121–2159 (Jul 2011)

2011

Earlier work this paper cites.

Le, Q.V., Ngiam, J., Coates, A., Lahiri, A., Prochnow, B., Ng, A.Y.: On optimization methods for deep learning. In: Proceedings of the 28th International Conference on International Conference on Machine Learning. pp. 265–272. ICML’11, Omnipress, USA (2011)

2011

Earlier work this paper cites.

Bellemare, M.G., Naddaf, Y., Veness, J., Bowling, M.: The arcade learning environment: An evaluation platform for general agents. CoRR abs/1207.4708 (2012)

Original

2012

Earlier work this paper cites.

Dean, J., Corrado, G.S., Monga, R., Chen, K., Devin, M., Le, Q.V., Mao, M.Z., Ranzato, M., Senior, A., Tucker, P., Yang, K., Ng, A.Y.: Large scale distributed deep networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1. pp. 1223–1231. NIPS’12, Curran Associates Inc., USA (2012)

2012

Earlier work this paper cites.

Tieleman, T., Hinton, G.: Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning (2012)

2012

Earlier work this paper cites.

Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., Riedmiller, M.: Playing atari with deep reinforcement learning. In: NIPS Deep Learning Workshop (2013)

2013

Earlier work this paper cites.

Sutskever, I., Martens, J., Dahl, G., Hinton, G.: On the importance of initialization and momentum in deep learning. In: Proceedings of the 30th International Conference on International Conference on Machine Learning - Volume 28. pp. III–1139–III–1147. ICML’13, JMLR.org (2013)

2013

Earlier work this paper cites.

Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. CoRR abs/1412.6980 (2014)

Original

2014

Earlier work this paper cites.

Seide, F., Fu, H., Droppo, J., Li, G., Yu, D.: 1-bit stochastic gradient descent and application to data-parallel distributed training of speech DNNs. In: Interspeech 2014 (September 2014)

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), https://www.tensorflow.org/ , software available from tensorflow.org

2015

Earlier work this paper cites.

Keuper, J., Pfreundt, F.: Asynchronous parallel stochastic gradient descent - A numeric core for scalable distributed machine learning algorithms. CoRR abs/1505.04956 (2015)

Original

2015

Earlier work this paper cites.

Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., 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 (Feb 2015)

2015

Earlier work this paper cites.

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)

Original

2015

Earlier work this paper cites.

Schulman, J., Levine, S., Moritz, P., Jordan, M.I., Abbeel, P.: Trust region policy optimization. CoRR abs/1502.05477 (2015)

Original

2015

Cited alongside, same era.

Strom, N.: Scalable distributed DNN training using commodity GPU cloud computing. In: INTERSPEECH. pp. 1488–1492. ISCA (2015)

2015

Cited alongside, same era.

Alistarh, D., Li, J., Tomioka, R., Vojnovic, M.: QSGD: Randomized quantization for communication-optimal stochastic gradient descent. CoRR abs/1610.02132 (2016)

Original

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)

Original

2016

Cited alongside, same era.

Bhardwaj, O., Cong, G.: Practical efficiency of asynchronous stochastic gradient descent. In: 2016 2nd Workshop on Machine Learning in HPC Environments (MLHPC). vol. 00, pp. 56–62 (Nov 2016)

Adamski, R., Grel, T., Klimek, M., Michalewski, H.: Atari games and intel processors. CoRR abs/1705.06936 (2017)

Original

2017

Later among the works it cites.

Aji, A.F., Heafield, K.: Sparse communication for distributed gradient descent. CoRR abs/1704.05021 (2017)

Original

2017

Later among the works it cites.

Babaeizadeh, M., Frosio, I., Tyree, S., Clemons, J., Kautz, J.: Reinforcement learning through asynchronous advantage actor-critic on a GPU. In: ICLR (2017)

2017

Later among the works it cites.

Bansal, T., Pachocki, J., Sidor, S., Sutskever, I., Mordatch, I.: Emergent complexity via multi-agent competition. CoRR abs/1710.03748 (2017)

Original

2017

Later among the works it cites.

Goyal, P., Dollár, P., Girshick, R.B., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., He, K.: Accurate, large minibatch SGD: training ImageNet in 1 hour. CoRR abs/1706.02677 (2017)