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Journal Articles Entropy Year : 2023

An Information-Theoretic Perspective on Intrinsic Motivation in Reinforcement Learning: A Survey

Abstract

The reinforcement learning (RL) research area is very active, with an important number of new contributions, especially considering the emergent field of deep RL (DRL). However, a number of scientific and technical challenges still need to be resolved, among which we acknowledge the ability to abstract actions or the difficulty to explore the environment in sparse-reward settings which can be addressed by intrinsic motivation (IM). We propose to survey these research works through a new taxonomy based on information theory: we computationally revisit the notions of surprise, novelty, and skill-learning. This allows us to identify advantages and disadvantages of methods and exhibit current outlooks of research. Our analysis suggests that novelty and surprise can assist the building of a hierarchy of transferable skills which abstracts dynamics and makes the exploration process more robust.

Dates and versions

hal-04093961 , version 1 (10-05-2023)

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Arthur Aubret, Laëtitia Matignon, Salima Hassas. An Information-Theoretic Perspective on Intrinsic Motivation in Reinforcement Learning: A Survey. Entropy, 2023, 25 (2), pp.327. ⟨10.3390/e25020327⟩. ⟨hal-04093961⟩
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