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To facilitate research in the direction of sample efficient reinforcement learning, we held the MineRL Competition on Sample Efficient Reinforcement Learning Using Human Priors at the Thirty-third Conference on Neural Information Processing Systems (NeurIPS 2019).
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Goal-based action priors
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Minecraft as an experimental world for AI in robotics
K. C. Aluru, S. Tellex, J. Oberlin, and J. MacGlashan · 2015
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Investigating human priors for playing video games
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Meta learning shared hierarchies
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Rainbow: Combining improvements in deep reinforcement learning
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Learning from demonstrations for real world reinforcement learning
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Learning to run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning
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Gotta learn fast: A new benchmark for generalization in RL
A. Nichol, V. Pfau, C. Hesse, O. Klimov, and J. Schulman · 2018
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The MineRL competition on sample efficient reinforcement learning using human priors
W. H. Guss, C. Codel*, K. Hofmann*, B. Houghton*, N. Kuno*, S. Milani*, S. Mohanty*, D. Perez Liebana*, R. Salakhutdinov*, N. Topin*, M. Veloso*, and P. Wang* · 2019
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MineRL: A large-scale dataset of Minecraft demonstrations
W. H. Guss*, B. Houghton*, N. Topin, P. Wang, C. Codel, M. Veloso, and R. Salakhutdinov · 2019
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Guaranteeing reproducibility in deep learning competitions
B. Houghton, S. Milani, N. Topin, W. H. Guss, K. Hofmann, D. Perez-Liebana, M. Veloso, and R. Salakhutdinov · 2019
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Obstacle tower: A generalization challenge in vision, control, and planning
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String diagrams for assembly planning
J. Master, E. Patterson, S. Yousfi, and A. Canedo · 2019
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The multi-agent reinforcement learning in Malmo (MARLO) competition
D. Perez-Liebana, K. Hofmann, S. P. Mohanty, N. Kuno, A. Kramer, S. Devlin, R. D. Gaina, and D. Ionita · 2018
Cited alongside, same era.
Reinforced imitation: Sample efficient deep reinforcement learning for mapless navigation by leveraging prior demonstrations
M. Pfeiffer, S. Shukla, M. Turchetta, C. Cadena, A. Krause, R. Siegwart, and J. Nieto · 2018
Cited alongside, same era.
Hierarchical and interpretable skill acquisition in multi-task reinforcement learning
T. Shu, C. Xiong, and R. Socher · 2018
Cited alongside, same era.
A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
D. Silver, T. Hubert, J. Schrittwieser, I. Antonoglou, M. Lai, A. Guez, M. Lanctot, L. Sifre, D. Kumaran, T. Graepel, et al · 2018
Cited alongside, same era.
Deep reinforcement learning from policy-dependent human feedback
D. Arumugam, J. K. Lee, S. Saskin, and M. L. Littman · 2019
Cited alongside, same era.
Improving deep reinforcement learning in Minecraft with action advice
S. J. Frazier and M. O. Riedl · 2019
Cited alongside, same era.
ChainerRL: A deep reinforcement learning library
Y. Fujita, T. Kataoka, P. Nagarajan, and T. Ishikawa · 2019
Cited alongside, same era.
Hierarchical deep q-network with forgetting from imperfect demonstrations in Minecraft
A. Skrynnik, A. Staroverov, E. Aitygulov, K. Aksenov, V. Davydov, and A. I. Panov · 2019
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Keeping your distance: Solving sparse reward tasks using self-balancing shaped rewards
A. Trott, S. Zheng, C. Xiong, and R. Socher · 2019
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Grandmaster level in StarCraft II using multi-agent reinforcement learning
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Leveraging human guidance for deep reinforcement learning tasks
R. Zhang, F. Torabi, L. Guan, D. H. Ballard, and P. Stone · 2019
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Playing minecraft with behavioural cloning
Anssi Kanervisto, Janne Karttunen, and Ville Hautamäki · 2020
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Sample efficient reinforcement learning through learning from demonstrations in Minecraft
C. Scheller, Y. Schraner, and M. Vogel · 2020
Closest in time.