Learning to Perceive the World Through Control: Empowerment-Based Representation Learning

arXiv:2605.30656v1 Announce Type: new Abstract: In many practical reinforcement learning environments, observations are far higher-dimensional than the variables that matter for control. In this work, we ask: can we learn representations that capture only control-relevant features of the environment? We study this question through the empowerment objective, which maximizes an agent's influence over the environment and is widely used for unsupervised skill learning. We show that empowerment agents induce two distinct representations -- forward and backward -- that capture complementary aspects

Source: arXiv cs.LG — read the full report at the original publisher.

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