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Scaling end-to-end reinforcement learning to control real robots from visionpresents a series of challenges, in particular in terms of sample efficiency.Against end-to-end learning, state representation learning can help learn acompact, efficient and relevant representation of states that speeds up policylearning, reducing the number of samples needed, and that is easier tointerpret. We evaluate several state representation learning methods on goalbased robotics tasks and propose a new unsupervised model that stacksrepresentations and combines strengths of several of these approaches. Thismethod encodes all the relevant features, performs on par or better thanend-to-end learning with better sample efficiency, and is robust tohyper-parameters change.

Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics