Methods and apparatuses for training a model based reinforcement learning model

Abstract

Embodiments described herein relate to a method and apparatus for training a model based reinforcement learning, MBRL, model for use in an environment. The method comprises obtaining a sequence of observations, ot, representative of the environment at a time t; estimating latent states st at time t using a representation model, wherein the representation model estimates the latent states st based on the previous latent states st−1, previous actions at−1 and the observations ot; generating modelled observations, om,t, using an observation model, wherein the observation model generates the modelled observations based on the respective latent states st, wherein the step of generating comprises determining means and standard deviations based on the latent states st; and minimizing a first loss function to update network parameters of the representation model and the observation model, wherein the first loss function comprises a component comparing the modelled observations, om,t to the respective observations ot.

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