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Learning Hierarchical World Models with Adaptive Temporal Abstractions from Discrete Latent Dynamics

Computer Science

Learning Hierarchical World Models with Adaptive Temporal Abstractions from Discrete Latent Dynamics

C. Gumbsch, N. Sajid, et al.

THICK (Temporal Hierarchies from Invariant Context Kernels) learns hierarchical world models with discrete latent dynamics: a low level that sparsely updates invariant contexts and a high level that predicts context changes, producing interpretable temporal abstractions and improved model-based RL and planning. Research conducted by Christian Gumbsch, Noor Sajid, Georg Martius, and Martin V. Butz.

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