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Model-based Reinforcement Learning (MBRL) is a promising framework for learning control in a data-efficient manner.
Benchmarking model-based reinforcement learning
Wang, T., Bao, X., Clavera, I., Hoang, J., Wen, Y., Langlois, E., Zhang, S., Zhang, G., Abbeel, P., and Ba, J. (2019) · 1907
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Ctrl-z: Recovering from instability in reinforcement learning
Dasagi, V., Bruce, J., Peynot, T., and Leitner, J. (2019) · 1910
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Algorithms for hyper-parameter optimization
Bergstra, J. S., Bardenet, R., Bengio, Y., and Kégl, B. (2011) · 2011
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PILCO: A Model-Based and Data-Efficient Approach to Policy Search
Deisenroth, M. P. and Rasmussen, C. E. (2011) · 2011
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Sequential model-based optimization for general algorithm configuration
Hutter, F., Hoos, H. H., and Leyton-Brown, K. (2011) · 2011
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Random search for hyper-parameter optimization
Bergstra, J. and Bengio, Y. (2012) · 2012
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Practical bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P. (2012) · 2012
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MuJoCo: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y. (2012) · 2012
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Learning navigation behaviors end-to-end with AutoRL
Chiang, H. L., Faust, A., Fiser, M., and Francis, A. (2019) · 2014
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Human-level control through deep reinforcement learning
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., and Hassabis, D. (2015) · 2015
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Non-stochastic best arm identification and hyperparameter optimization
Jamieson, K. and Talwalkar, A. (2016) · 2016
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Bayesian optimization with robust bayesian neural networks
Springenberg, J. T., Klein, A., Falkner, S., and Hutter, F. (2016) · 2016
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Reproducibility of benchmarked deep reinforcement learning tasks for continuous control
Islam, R., Henderson, P., Gomrokchi, M., and Precup, D. (2017) · 2017
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Population based training of neural networks
Jaderberg, M., Dalibard, V., Osindero, S., Czarnecki, W. M., Donahue, J., Razavi, A., Vinyals, O., Green, T., Dunning, I., Simonyan, K., et al. (2017) · 2017
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Fast bayesian optimization of machine learning hyperparameters on large datasets
Klein, A., Falkner, S., Bartels, S., Hennig, P., and Hutter, F. (2017) · 2017
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BOHB: robust and efficient hyperparameter optimization at scale
Falkner, S., Klein, A., and Hutter, F. (2018) · 2018
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Deep reinforcement learning that matters
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., and Meger, D. (2018) · 2018
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Model-ensemble trust-region policy optimization
Kurutach, T., Clavera, I., Duan, Y., Tamar, A., and Abbeel, P. (2018) · 2018
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On the state of the art of evaluation in neural language models
Melis, G., Dyer, C., and Blunsom, P. (2018) · 2018
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Hyperparameter optimization
Feurer, M. and Hutter, F. (2019) · 2019
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Multi-fidelity gaussian process bandit optimisation
Kandasamy, K., Dasarathy, G., Oliva, J. B., Schneider, J. G., and Póczos, B. (2019) · 2019
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Hyperband: A novel bandit-based approach to hyperparameter optimization
Li, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., and Talwalkar, A. (2017) · 2017
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Information theoretic MPC for model-based reinforcement learning
Williams, G., Wagener, N., Goldfain, B., Drews, P., Rehg, J. M., Boots, B., and Theodorou, E. A. (2017) · 2017
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Differentiable MPC for end-to-end planning and control
Amos, B., Rodriguez, I. D. J., Sacks, J., Boots, B., and Kolter, J. Z. (2018) · 2018
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Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Chua, K., Calandra, R., McAllister, R., and Levine, S. (2018) · 2018
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Fast efficient hyperparameter tuning for policy gradient methods
Paul, S., Kurin, V., and Whiteson, S. (2019) · 2019
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Learning to design RNA
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Objective mismatch in model-based reinforcement learning
Lambert, N., Amos, B., Yadan, O., and Calandra, R. (2020) · 2020
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