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Curriculum learning is a training mechanism in reinforcement learning (RL) that facilitates the achievement of complex policies by progressively increasing the task difficulty during training.
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2022
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2024
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2024
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2024
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W. Liang, S. Wang, H.-J. Wang, Y. J. Ma, O. Bastani, and D. Jayaraman, “Environment curriculum generation via large language models,” in 8th Annual Conference on Robot Learning
2024
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Y. J. Ma, W. Liang, G. Wang, D.-A. Huang, O. Bastani, D. Jayaraman, Y. Zhu, L. Fan, and A. Anandkumar, “Eureka: Human-level reward design via coding large language models,” in The Twelfth International Conference on Learning Representations
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2024
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E. Clark, K. Ryu, and N. Mehr, “Adaptive teaching in heterogeneous agents: Balancing surprise in sparse reward scenarios,” in Proceedings of the 6th Annual Learning for Dynamics & Control Conference
2024
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2024
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2024
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2024
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