Publication Details

SELECT * FROM publications WHERE Record_Number=2574
Reference TypeConference Proceedings
Author(s)Peters, J.;Vijayakumar, S.;Schaal, S.
Year2005
TitleNatural Actor-Critic
Journal/Conference/Book TitleProceedings of the 16th European Conference on Machine Learning (ECML 2005)
KeywordsReinforcement Learning, Policy Gradients, Natural Gradients
AbstractThis paper investigates a novel model-free reinforcement learning architecture, the Natural Actor-Critic. The actor updates are based on stochastic policy gradients employing Amari�s natural gradient approach, while the critic obtains both the natural policy gradient and additional parameters of a value function simultaneously by linear regres- sion. We show that actor improvements with natural policy gradients are particularly appealing as these are independent of coordinate frame of the chosen policy representation, and can be estimated more efficiently than regular policy gradients. The critic makes use of a special basis function parameterization motivated by the policy-gradient compatible function approximation. We show that several well-known reinforcement learning methods such as the original Actor-Critic and Bradtke�s Linear Quadratic Q-Learning are in fact Natural Actor-Critic algorithms. Em- pirical evaluations illustrate the effectiveness of our techniques in com- parison to previous methods, and also demonstrate their applicability for learning control on an anthropomorphic robot arm.
Link to PDFhttps://www.ias.informatik.tu-darmstadt.de/uploads/Publications/Publications/NaturalActorCritic.pdf

  

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