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In deep reinforcement learning, building policies of high-quality is challenging when the feature space of states is small and the training data is limited.
Reinforcement learning - an introduction
Sutton, R. S. and Barto, A. G · 1998
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Reinforcement learning for mapping instructions to actions
Branavan, S. R. K., Chen, H., Zettlemoyer, L. S., and Barzilay, R · 2009
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Transfer learning for reinforcement learning domains: A survey
Taylor, M. E. and Stone, P · 2009
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Privacy aware learning
Duchi, J. C., Jordan, M. I., and Wainwright, M. J · 2012
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The algorithmic foundations of differential privacy
Dwork, C. and Roth, A · 2014
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Action-model acquisition for planning via transfer learning
Zhuo, H. H. and Yang, Q · 2014
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Learning hierarchical task network domains from partially observed plan traces
Zhuo, H. H., Muñoz-Avila, H., and Yang, Q · 2014
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Federated optimization: Distributed optimization beyond the datacenter
Konecný, J., McMahan, B., and Ramage, D · 2015
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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., Fidjeland, A. K., and Ostrovski, G · 2015
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Multiagent cooperation and competition with deep reinforcement learning
Tampuu, A., Matiisen, T., Kodelja, D., Kuzovkin, I., Korjus, K., Aru, J., Aru, J., and Vicente, R · 2015
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Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I. J., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
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Learning to communicate with deep multi-agent reinforcement learning
Foerster, J. N., Assael, Y. M., de Freitas, N., and Whiteson, S · 2016
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Federated learning: Strategies for improving communication efficiency
Konecný, J., McMahan, H. B., Yu, F. X., Richtárik, P., Suresh, A. T., and Bacon, D · 2016
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Value iteration networks
Tamar, A., Levine, S., Abbeel, P., Wu, Y., and Thomas, G · 2016
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Federated multi-task learning
Smith, V., Chiang, C., Sanjabi, M., and Talwalkar, A. S · 2017
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Model-lite planning: Case-based vs. model-based approaches
Zhuo, H. H. and Kambhampati, S · 2017
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cpsgd: Communication-efficient and differentially-private distributed SGD
Agarwal, N., Suresh, A. T., Yu, F. X., Kumar, S., and McMahan, B · 2018
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Self-consistent trajectory autoencoder: Hierarchical reinforcement learning with trajectory embeddings
Co-Reyes, J., Liu, Y., Gupta, A., Eysenbach, B., Abbeel, P., and Levine, S · 2018
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Extracting action sequences from texts based on deep reinforcement learning
Feng, W., Zhuo, H. H., and Kambhampati, S · 2018
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QMIX: monotonic value function factorisation for deep multi-agent reinforcement learning
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Multi-agent reinforcement learning in sequential social dilemmas
Leibo, J. Z., Zambaldi, V. F., Lanctot, M., Marecki, J., and Graepel, T · 2017
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Multi-agent actor-critic for mixed cooperative-competitive environments
Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., and Mordatch, I · 2017
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Communication-efficient learning of deep networks from decentralized data
McMahan, B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
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Deep decentralized multi-task multi-agent reinforcement learning under partial observability
Omidshafiei, S., Pazis, J., Amato, C., How, J. P., and Vian, J · 2017
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Rashid, T., Samvelyan, M., de Witt, C. S., Farquhar, G., Foerster, J. N., and Whiteson, S · 2018
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Transfer of value functions via variational methods
Tirinzoni, A., Rodriguez Sanchez, R., and Restelli, M · 2018
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Fully convolutional network with multi-step reinforcement learning for image processing
Furuta, R., Inoue, N., and Yamasaki, T · 2019
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Federated machine learning: Concept and applications
Yang, Q., Liu, Y., Chen, T., and Tong, Y · 2019
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