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Covariant Policy Search
J. Bagnell and J. Schneider
Proceeding of the International Joint Conference on Artifical Intelligence, August, 2003.

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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.

Associated lab/group: Auton Lab
Associated project: Auton Project

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

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