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With AlphaGo defeats top human players, reinforcement learning(RL) algorithms have gradually become the code-base of building stronger artificial intelligence(AI).
Dyna, an integrated architecture for learning, planning, and reacting
Richard S. Sutton · 1991
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The benefit of multitask representation learning
Andreas Maurer, Massimiliano Pontil, and Bernardino Romera-Paredes · 2016
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Andrei A. Rusu, Neil C. Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
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Learning to reinforcement learn
Jane X. Wang, Zeb Kurth-Nelson, Dhruva Tirumala, Hubert Soyer, Joel Z. Leibo, Remi Munos, Charles Blundell, Dharshan Kumaran, and Matt Botvinick · 2016
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Continuous deep q-learning with model-based acceleration
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John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, Pieter Abbeel, and Wojciech Zaremba · 2017
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Meire Fortunato, Mohammad Gheshlaghi Azar, Bilal Piot, Jacob Menick, Ian Osband, Alex Graves, Vlad Mnih, Remi Munos, Demis Hassabis, and Olivier Pietquin · 2017
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# exploration: A study of count-based exploration for deep reinforcement learning
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Distral: Robust multitask reinforcement learning
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Carlos Florensa, Yan Duan, and Pieter Abbeel · 2017
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Model-based value estimation for efficient model-free reinforcement learning
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Dota 2 with large scale deep reinforcement learning
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A survey of exploration strategies in reinforcement learning
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Reinforcement learning: An introduction
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Grandmaster level in starcraft ii using multi-agent reinforcement learning
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Human-level performance in 3d multiplayer games with population-based reinforcement learning
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Self-supervised exploration via disagreement
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Open-ended learning leads to generally capable agents
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