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Off-policy reinforcement learning (RL) using a fixed offline dataset of logged interactions is an important consideration in real world applications.
Benchmarking batch deep reinforcement learning algorithms
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Markov Decision Processes: Discrete Stochastic Dynamic Programming
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Tree-based batch mode reinforcement learning
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Neural fitted q iteration–first experiences with a data efficient neural reinforcement learning method
Riedmiller, M · 2005
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ImageNet: A Large-Scale Hierarchical Image Database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Learning from logged implicit exploration data
Strehl, A. L., Langford, J., Li, L., and Kakade, S · 2010
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Informing sequential clinical decision-making through reinforcement learning: an empirical study
Shortreed, S. M., Laber, E., Lizotte, D. J., Stroup, T. S., Pineau, J., and Murphy, S. A · 2011
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Batch reinforcement learning
Lange, S., Gabel, T., and Riedmiller, M · 2012
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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
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Mujoco: A physics engine for model-based control
Todorov, E., Erez, T., and Tassa, Y · 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
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Counterfactual reasoning and learning systems: The example of computational advertising
Bottou, L., Peters, J., Quiñonero-Candela, J., Charles, D. X., Chickering, D. M., Portugaly, E., Ray, D., Simard, P., and Snelson, E · 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
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Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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The importance of experience replay database composition in deep reinforcement learning
De Bruin, T., Kober, J., Tuyls, K., and Babuška, R · 2015
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Neural network ensembles in reinforcement learning
Faußer, S. and Schwenker, F · 2015
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A comprehensive survey on safe reinforcement learning
Garcıa, J. and Fernández, F · 2015
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Adam: A method for stochastic optimization
Addressing function approximation error in actor-critic methods
Fujimoto, S., van Hoof, H., and Meger, D · 2018
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Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor
Haarnoja, T., Zhou, A., Abbeel, P., and Levine, S · 2018
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Revisiting the arcade learning environment: Evaluation protocols and open problems for general agents
Machado, M. C., Bellemare, M. G., Talvitie, E., Veness, J., Hausknecht, M., and Bowling, M · 2018
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Organizing experience: a deeper look at replay mechanisms for sample-based planning in continuous state domains
Pan, Y., Zaheer, M., White, A., Patterson, A., and White, M · 2018
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Reinforcement learning: An introduction
Sutton, R. S. and Barto, A. G · 2018
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Kingma, D. P. and Ba, J · 2015
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Continuous control with deep reinforcement learning
Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., Silver, D., and Wierstra, 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., Ostrovski, G., et al · 2015
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Batch learning from logged bandit feedback through counterfactual risk minimization
Swaminathan, A. and Joachims, T · 2015
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Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W · 2016
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Deep exploration via bootstrapped dqn
Osband, I., Blundell, C., Pritzel, A., and Van Roy, B · 2016
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Prioritized experience replay
Schaul, T., Quan, J., Antonoglou, I., and Silver, D · 2016
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Policy gradient algorithms
Weng, L · 2018
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Bdd100k: A diverse driving video database with scalable annotation tooling
Yu, F., Xian, W., Chen, Y., Liu, F., Liao, M., Madhavan, V., and Darrell, T · 2018
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A framework for data-driven robotics
Cabi, S., Colmenarejo, S. G., Novikov, A., Konyushkova, K., Reed, S., Jeong, R., Żołna, K., Aytar, Y., Budden, D., Vecerik, M., et al · 2019
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Robonet: Large-scale multi-robot learning
Dasari, S., Ebert, F., Tian, S., Nair, S., Bucher, B., Schmeckpeper, K., Singh, S., Levine, S., and Finn, C · 2019
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Challenges of real-world reinforcement learning
Dulac-Arnold, G., Mankowitz, D., and Hester, T · 2019
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Way off-policy batch deep reinforcement learning of implicit human preferences in dialog
Jaques, N., Ghandeharioun, A., Shen, J. H., Ferguson, C., Lapedriza, A., Jones, N., Gu, S., and Picard, R · 2019
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Model-based reinforcement learning for atari
Kaiser, L., Babaeizadeh, M., Milos, P., Osinski, B., Campbell, R. H., Czechowski, K., Erhan, D., Finn, C., Kozakowski, P., Levine, S., et al · 2019
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Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction
Kumar, A., Fu, J., Tucker, G., and Levine, S · 2019
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Safe policy improvement with baseline bootstrapping
Laroche, R., Trichelair, P., and Combes, R. T. d · 2019
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Off-policy policy gradient with state distribution correction
Liu, Y., Swaminathan, A., Agarwal, A., and Brunskill, E · 2019
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When to use parametric models in reinforcement learning?
Van Hasselt, H., Hessel, M., and Aslanides, J · 2019
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Behavior regularized offline reinforcement learning
Wu, Y., Tucker, G., and Nachum, O · 2019
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Keep doing what worked: Behavior modelling priors for offline reinforcement learning
Siegel, N., Springenberg, J. T., Berkenkamp, F., Abdolmaleki, A., Neunert, M., Lampe, T., Hafner, R., Heess, N., and Riedmiller, M · 2020
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