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Found 4 papers, 2 papers with code
The Power of Learned Locally Linear Models for Nonlinear Policy Optimization
A common pipeline in learning-based control is to iteratively estimate a model of system dynamics, and apply a trajectory optimization algorithm - e. g.~$\mathtt{iLQR}$ - on the learned model to minimize a target cost.
TaSIL: Taylor Series Imitation Learning
We propose Taylor Series Imitation Learning (TaSIL), a simple augmentation to standard behavior cloning losses in the context of continuous control.
Linear Variational State-Space Filtering
We introduce Variational State-Space Filters (VSSF), a new method for unsupervised learning, identification, and filtering of latent Markov state space models from raw pixels.
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