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Causal Discovery for Modular World Models
Anson Lei · Bernhard Schölkopf · Ingmar Posner

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in Workshop on neuro Causal and Symbolic AI (nCSI) »
Event URL: https://openreview.net/forum?id=VfkjQzdGCH »

Latent world models allow agents to reason about complex environments with high-dimensional observations. However, adapting to new environments and effectively leveraging previous knowledge remain significant challenges. We present variational causal dynamics (VCD), a structured world model that exploits the invariance of causal mechanisms across environments to achieve fast and modular adaptation. VCD identifies reusable components across different environments by combining causal discovery and variational inference to learn a latent representation and transition model jointly in an unsupervised manner. In evaluations on simulated environments with image observations, we show that VCD is able to successfully identify causal variables. Moreover, given a small number of observations in a previously unseen, intervened environment, VCD is able to identify the sparse changes in the dynamics and to adapt efficiently. In doing so, VCD significantly extends the capabilities of the current state-of-the-art in latent world models.

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Author Information

Anson Lei (University of Oxford)
Bernhard Schölkopf (MPI for Intelligent Systems, Tübingen)

Bernhard Scholkopf received degrees in mathematics (London) and physics (Tubingen), and a doctorate in computer science from the Technical University Berlin. He has researched at AT&T Bell Labs, at GMD FIRST, Berlin, at the Australian National University, Canberra, and at Microsoft Research Cambridge (UK). In 2001, he was appointed scientific member of the Max Planck Society and director at the MPI for Biological Cybernetics; in 2010 he founded the Max Planck Institute for Intelligent Systems. For further information, see www.kyb.tuebingen.mpg.de/~bs.

Ingmar Posner (Oxford University)

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