Probabilistic World Modeling with Asymmetric Distance Measure

arXiv:2403.10875v1 Announce Type: new
Abstract: Representation learning is a fundamental task in machine learning, aiming at uncovering structures from data to facilitate subsequent tasks. However, what is a good representation for planning and reasoning in a stochastic world remains an open problem. In this work, we posit that learning a distance function is essential to allow planning and reasoning in the representation space. We show that a geometric abstraction of the probabilistic world dynamics can be embedded into the representation …

abstract arxiv cs.lg data function good however machine machine learning modeling planning posit reasoning representation representation learning stochastic tasks type work world