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Learning Fast Adaptation with Meta Strategy Optimization
[article]
2020
arXiv
pre-print
The ability to walk in new scenarios is a key milestone on the path toward real-world applications of legged robots. In this work, we introduce Meta Strategy Optimization, a meta-learning algorithm for training policies with latent variable inputs that can quickly adapt to new scenarios with a handful of trials in the target environment. The key idea behind MSO is to expose the same adaptation process, Strategy Optimization (SO), to both the training and testing phases. This allows MSO to
arXiv:1909.12995v2
fatcat:3kndegpgjfe6nbqq4azmmgbr7u