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Humans and animals explore their environment and acquire useful skills evenin the absence of clear goals, exhibiting intrinsic motivation. The study ofintrinsic motivation in artificial agents is concerned with the followingquestion: what is a good general-purpose objective for an agent? We study thisquestion in dynamic partially-observed environments, and argue that a compactand general learning objective is to minimize the entropy of the agent's statevisitation estimated using a latent state-space model. This objective inducesan agent to both gather information about its environment, corresponding toreducing uncertainty, and to gain control over its environment, correspondingto reducing the unpredictability of future world states. We instantiate thisapproach as a deep reinforcement learning agent equipped with a deepvariational Bayes filter. We find that our agent learns to discover, represent,and exercise control of dynamic objects in a variety of partially-observedenvironments sensed with visual observations without extrinsic reward.

Information is Power: Intrinsic Control via Information Capture