Interactive Differentiable Simulation

26 May 2019Eric HeidenDavid MillardHejia ZhangGaurav S. Sukhatme

Intelligent agents need a physical understanding of the world to predict the impact of their actions in the future. While learning-based models of the environment dynamics have contributed to significant improvements in sample efficiency compared to model-free reinforcement learning algorithms, they typically fail to generalize to system states beyond the training data, while often grounding their predictions on non-interpretable latent variables... (read more)

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