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Learning Latent Dynamics via Invariant Decomposition and (Spatio-)Temporal Transformers
We focus on the setting in which data are available from multiple different instances of a system whose underlying dynamical model is entirely unknown at the outset.
Deep Learning of Causal Structures in High Dimensions
Recent years have seen rapid progress at the intersection between causality and machine learning.
AUGMENTED POLICY GRADIENT METHODS FOR EFFICIENT REINFORCEMENT LEARNING
The influence of the ensemble of dynamics models on the policy update is controlled by adjusting the number of virtually performed rollouts in the next iteration according to the ratio of the real and virtual total reward.
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