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Carnegie Mellon Robotics Institute

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Abstract

We investigate the problem of non-covariant behavior of policy gradient reinforcement learning algorithms. The policy gradient approach is amenable to analysis by information geometric methods. This leads us to propose a natural metric on controller parameterization that results from considering the manifold of probability distributions over paths induced by a stochastic controller. Investigation of this approach leads to a covariant gradient ascent rule. Interesting properties of this rule are discussed, including its relation with actor-critic style reinforcement learning algorithms. The algorithms discussed here are computationally quite efficient and on some interesting problems lead to dramatic performance improvement over non-covariant rules.

Keywords

Reinforcement Learning, Reinforce, Natural gradient, covariant

Notes

Associated Lab(s) / Group(s): Auton Lab

Associated Project(s): Auton Project

Text Reference

J. Andrew (Drew) Bagnell and Jeff Schneider, "Covariant Policy Search," Proceeding of the International Joint Conference on Artifical Intelligence, August, 2003.

BibTeX Reference

@inproceedings{Bagnell_2003_4486,
   author = "J. Andrew (Drew) Bagnell and Jeff Schneider",
   title = "Covariant Policy Search",
   booktitle = "Proceeding of the International Joint Conference on Artifical Intelligence",
   month = "August",
   year = "2003",
}

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