Open-Ended Learning: A Conceptual Framework Based on Representational Redescription

Open-Ended Learning: A Conceptual Framework Based on Representational Redescription

Reinforcement learning (RL) aims at building a policy that maximizes a task-related reward within a given domain. When the domain is known, i.e., when its states, actions and reward are defined, Markov Decision Processes (MDPs) provide a convenient theoretical framework to formalize RL. But in an op...

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Bibliographic Details
Main Authors: Stephane Doncieux, David Filliat, Natalia Díaz-Rodríguez, Timothy Hospedales, Richard Duro, Alexandre Coninx, Diederik M. Roijers, Benoît Girard, Nicolas Perrin, Olivier Sigaud
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-09-01
Series:Frontiers in Neurorobotics
Subjects:
developmental robotics
reinforcement learning
state representation learning
representational redescription
actions and goals
skills
Online Access:https://www.frontiersin.org/article/10.3389/fnbot.2018.00059/full

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https://www.frontiersin.org/article/10.3389/fnbot.2018.00059/full

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