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Model-free deep reinforcement learning (RL) algorithms have been successfully applied to a range of challenging sequential decision making and control tasks.
Neuronlike adaptive elements that can solve difficult learning control problems
Barto, A. G., Sutton, R. S., and Anderson, C. W · 1983
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Simple statistical gradient-following algorithms for connectionist reinforcement learning
Williams, R. J · 1992
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Reinforcement learning: An introduction , volume 1
Sutton, R. S. and Barto, A. G · 1998
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Convex optimization
Boyd, S. and Vandenberghe, L · 2004
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Path integrals and symmetry breaking for optimal control theory
Kappen, H. J · 2005
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Linearly-solvable Markov decision problems
Todorov, E · 2007
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Reinforcement learning of motor skills with policy gradients
Peters, J. and Schaal, S · 2008
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General duality between optimal control and estimation
Todorov, E · 2008
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Maximum entropy inverse reinforcement learning
Ziebart, B. D., Maas, A. L., Bagnell, J. A., and Dey, A. K · 2008
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Convergent temporal-difference learning with arbitrary smooth function approximation
Bhatnagar, S., Precup, D., Silver, D., Sutton, R. S., Maei, H. R., and Szepesvári, C · 2009
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Robot trajectory optimization using approximate inference
Toussaint, M · 2009
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Double Q-learning
Hasselt, H. V · 2010
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Modeling purposeful adaptive behavior with the principle of maximum causal entropy
Ziebart, B. D · 2010
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On stochastic optimal control and reinforcement learning by approximate inference
Rawlik, K., Toussaint, M., and Vijayakumar, S · 2012
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Guided policy search
Levine, S. and Koltun, V · 2013
Cited alongside, same era.
Playing atari with deep reinforcement learning
Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., and Riedmiller, M · 2013
Cited alongside, same era.
Deterministic policy gradient algorithms
Silver, D., Lever, G., Heess, N., Degris, T., Wierstra, D., and Riedmiller, M · 2014
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Bias in natural actor-critic algorithms
Thomas, P · 2014
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Learning continuous control policies by stochastic value gradients
Heess, N., Wayne, G., Silver, D., Lillicrap, T., Erez, T., and Tassa, Y · 2015
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2015
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Design principles for a family of direct-drive legged robots
Kenneally, G., De, A., and Koditschek, D. E · 2016
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End-to-end training of deep visuomotor policies
Levine, S., Finn, C., Darrell, T., and Abbeel, P · 2016
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Asynchronous methods for deep reinforcement learning
Mnih, V., Badia, A. P., Mirza, M., Graves, A., Lillicrap, T. P., Harley, T., Silver, D., and Kavukcuoglu, K · 2016
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PGQ: Combining policy gradient and Q-learning
O’Donoghue, B., Munos, R., Kavukcuoglu, K., and Mnih, V · 2016
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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
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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
Cited alongside, same era.
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
Cited alongside, same era.
Trust region policy optimization
Schulman, J., Levine, S., Abbeel, P., Jordan, M. I., and Moritz, P · 2015
Cited alongside, same era.
Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., and Zaremba, W · 2016
Cited alongside, same era.
Benchmarking deep reinforcement learning for continuous control
Duan, Y., Chen, X. Houthooft, R., Schulman, J., and Abbeel, P · 2016
Cited alongside, same era.
Taming the noise in reinforcement learning via soft updates
Fox, R., Pakman, A., and Tishby, N · 2016
Cited alongside, same era.
The reactor: A sample-efficient actor-critic architecture
Gruslys, A., Azar, M. G., Bellemare, M. G., and Munos, R · 2017
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Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates
Gu, S., Holly, E., Lillicrap, T., and Levine, S · 2017
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Reinforcement learning with deep energy-based policies
Haarnoja, T., Tang, H., Abbeel, P., and Levine, S · 2017
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Deep reinforcement learning that matters
Henderson, P., Islam, R., Bachman, P., Pineau, J., Precup, D., and Meger, D · 2017
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Maximum a posteriori policy optimisation
Abdolmaleki, A., Springenberg, J. T., Tassa, Y., Munos, R., Heess, N., and Riedmiller, M · 2018
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Addressing function approximation error in actor-critic methods
Fujimoto, S., van Hoof, H., and Meger, D · 2018
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Sim-to-real: Learning agile locomotion for quadruped robots
Tan, J., Zhang, T., Coumans, E., Iscen, A., Bai, Y., Hafner, D., Bohez, S., and Vanhoucke, V · 2018
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Dexterous manipulation with deep reinforcement learning: Efficient, general, and low-cost
Zhu, H., Gupta, A., Rajeswaran, A., Levine, S., and Kumar, V · 2018
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