Posts Tagged ‘Control’

Manuel Watter, Jost Tobias Springenberg, Joschka Boedecker, Martin Riedmiller:
Embed to Control: A Locally Linear Latent Dynamics Model for Control from Raw Images
arXiv, 2015

This paper deals with the problem of model-based reinforcement learning (RL) from images. The idea behind model-based RL is to learn a model of the transition dynamics of the system/robot and use this model as a surrogate simulator. This is helpful if we want to minimize experiments with a (physical/mechanical) system. The added difficulty addressed in this paper is that this predictive transition model should be learned from raw images where only pixel information is available.

Central idea (more…)