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In this work, we investigate the application of Taylor expansions in reinforcement learning.
Policy optimization through approximated importance sampling
Tomczak, M. B., Kim, D., Vrancx, P., and Kim, K.-E. (2019) · 1910
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Dota 2 with large scale deep reinforcement learning
Berner, C., Brockman, G., Chan, B., Cheung, V., Debiak, P., Dennison, C., Farhi, D., Fischer, Q., Hashme, S., Hesse, C., et al. (2019) · 1912
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Eligibility traces for off-policy policy evaluation
Precup, D., Sutton, R. S., and Singh, S. P. (2000) · 2000
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Policy gradient methods for reinforcement learning with function approximation
Sutton, R. S., McAllester, D. A., Singh, S. P., and Mansour, Y. (2000) · 2000
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Approximately optimal approximate reinforcement learning
Kakade, S. and Langford, J. (2002) · 2002
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On a connection between importance sampling and the likelihood ratio policy gradient
Jie, T. and Abbeel, P. (2010) · 2010
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude
Tieleman, T. and Hinton, G. (2012) · 2012
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The arcade learning environment: An evaluation platform for general agents
Bellemare, M. G., Naddaf, Y., Veness, J., and Bowling, M. (2013) · 2013
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Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M. (2013) · 2013
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2014) · 2014
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Massively parallel methods for deep reinforcement learning
Nair, A., Srinivasan, P., Blackwell, S., Alcicek, C., Fearon, R., De Maria, A., Panneershelvam, V., Suleyman, M., Beattie, C., Petersen, S., et al. (2015) · 2015
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Trust region policy optimization
Schulman, J., Levine, S., Abbeel, P., Jordan, M., and Moritz, P. (2015) · 2015
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Q( λ \lambda ) with Off-Policy Corrections
Harutyunyan, A., Bellemare, M. G., Stepleton, T., and Munos, R. (2016) · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J. (2016) · 2016
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Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T., Harley, T., Silver, D., and Kavukcuoglu, K. (2016) · 2016
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Safe and efficient off-policy reinforcement learning
Munos, R., Stepleton, T., Harutyunyan, A., and Bellemare, M. (2016) · 2016
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High-dimensional continuous control using generalized advantage estimation
Schulman, J., Moritz, P., Levine, S., Jordan, M., and Abbeel, P. (2016) · 2016
Maximum a posteriori policy optimisation
Abdolmaleki, A., Springenberg, J. T., Tassa, Y., Munos, R., Heess, N., and Riedmiller, M. (2018) · 2018
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Distributional policy gradients
Barth-Maron, G., Hoffman, M. W., Budden, D., Dabney, W., Horgan, D., TB, D., Muldal, A., Heess, N., and Lillicrap, T. (2018) · 2018
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IMPALA: Scalable distributed deep-RL with importance weighted actor-learner architectures
Espeholt, L., Soyer, H., Munos, R., Simonyan, K., Mnih, V., Ward, T., Doron, Y., Firoiu, V., Harley, T., Dunning, I., Legg, S., and Kavukcuoglu, K. (2018) · 2018
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The reactor: A fast and sample-efficient actor-critic agent for reinforcement learning
Gruslys, A., Dabney, W., Azar, M. G., Piot, B., Bellemare, M., and Munos, R. (2018) · 2018
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Distributed prioritized experience replay
Horgan, D., Quan, J., Budden, D., Barth-Maron, G., Hessel, M., van Hasselt, H., and Silver, D. (2018) · 2018
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Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T., and Hassabis, D. (2016) · 2016
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Dueling network architectures for deep reinforcement learning
Wang, Z., Schaul, T., Hessel, M., Hasselt, H., Lanctot, M., and Freitas, N. (2016) · 2016
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Reinforcement learning through asynchronous advantage actor-critic on a gpu
Babaeizadeh, M., Frosio, I., Tyree, S., Clemons, J., and Kautz, J. (2017) · 2017
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Proximal policy optimization algorithms
Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. (2017) · 2017
Cited alongside, same era.
Sample efficient actor-critic with experience replay
Wang, Z., Bapst, V., Heess, N., Mnih, V., Munos, R., Kavukcuoglu, K., and de Freitas, N. (2017) · 2017
Cited alongside, same era.
Metelli, A. M., Papini, M., Faccio, F., and Restelli, M. (2018) · 2018
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Observe and look further: Achieving consistent performance on atari
Pohlen, T., Piot, B., Hester, T., Azar, M. G., Horgan, D., Budden, D., Barth-Maron, G., Van Hasselt, H., Quan, J., Večerík, M., et al. (2018) · 2018
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Recurrent experience replay in distributed reinforcement learning
Kapturowski, S., Ostrovski, G., Dabney, W., Quan, J., and Munos, R. (2019) · 2019
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
Vinyals, O., Babuschkin, I., Czarnecki, W. M., Mathieu, M., Dudzik, A., Chung, J., Choi, D. H., Powell, R., Ewalds, T., Georgiev, P., et al. (2019) · 2019
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Adaptive trade-offs in off-policy learning
Rowland, M., Dabney, W., and Munos, R. (2020) · 2020
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V-MPO: on-policy maximum a posteriori policy optimization for discrete and continuous control
Song, H. F., Abdolmaleki, A., Springenberg, J. T., Clark, A., Soyer, H., Rae, J. W., Noury, S., Ahuja, A., Liu, S., Tirumala, D., Heess, N., Belov, D., Riedmiller, M., and Botvinick, M. M. (2020) · 2020
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