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Workshop: Information-Theoretic Principles in Cognitive Systems

An information-theoretic perspective on intrinsic motivation in reinforcement learning

Arthur Aubret · Laetitia Matignon · Salima Hassas


The standard reinforcement learning (RL) framework faces the problem of transfer learning and sparse rewards explorations. To address these problems, a large number of heterogeneous intrinsic motivation have been proposed, like reaching unpredictable states or unvisited states. Yet, it lacks a coherent view on these intrinsic motivations, making hard to understand their relations as well as their underlying assumptions. Here, we propose a new taxonomy of intrinsic motivations based on information theory: we computationally revisit the notions of surprise, novelty and skill learning and identify their main implementations through a short review of intrinsic motivations in RL. Our information theoretic analysis paves the way towards an unifying view over complex behaviors, thereby supporting the development of new objective functions.

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