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Carlos Florensa

Reverse Curriculum Generation for Reinforcement Learning
2017 · Florensa, Carlos, Held, David, et al. · arXiv (Cornell University) · 277
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Stochastic Neural Networks for Hierarchical Reinforcement Learning
2017 · Florensa, Carlos, Duan, Yan, et al. · arXiv (Cornell University) · 194
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Automatic Goal Generation for Reinforcement Learning Agents
2017 · Florensa, Carlos, Held, David, et al. · arXiv (Cornell University) · 193
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Automatic Goal Generation for Reinforcement Learning Agents
2018 · Carlos Florensa, David Held, et al. · International Conference on Machine Learning · 127
Goal-Conditioned Imitation Learning
2019 · Ding, Yiming, Florensa, Carlos, et al. · arXiv (Cornell University) · 119
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Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning
2020 · Michelle A. Lee, Carlos Florensa, et al. · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · 57
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Stochastic Neural Networks for Hierarchical Reinforcement Learning
2016 · Carlos Florensa, Yan Duan, et al. · International Conference on Learning Representations · 57
Sub-policy Adaptation for Hierarchical Reinforcement Learning
2019 · Li, Alexander C., Florensa, Carlos, et al. · arXiv (Cornell University) · 46
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Self-supervised Learning of Image Embedding for Continuous Control
2019 · Florensa, Carlos, Degrave, Jonas, et al. · arXiv (Cornell University) · 27
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Capacity planning with competitive decision-makers: Trilevel MILP formulation, degeneracy, and solution approaches
2017 · Carlos Florensa, Pablo Garcia-Herreros, et al. · European Journal of Operational Research · 26
Which Mutual-Information Representation Learning Objectives are Sufficient for Control?
2021 · Rakelly, Kate, Gupta, Abhishek, et al. · arXiv (Cornell University) · 10
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The magic of light! An entertaining optics and photonics awareness program
2014 · Carlos Florensa, Míriam Martí, et al. · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 5
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Adaptive Variance for Changing Sparse-Reward Environments
2019 · Xingyu Lin, Pengsheng Guo, et al. · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · 4
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Works count: 14
Citation count: 1,142
H-index: 10
I10-index: 11
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