Natural Policy Gradient Methods with Parameter-based Exploration for Control Tasks

Part of Advances in Neural Information Processing Systems 23 (NIPS 2010)

Bibtex Metadata Paper


Atsushi Miyamae, Yuichi Nagata, Isao Ono, Shigenobu Kobayashi


In this paper, we propose an efficient algorithm for estimating the natural policy gradient with parameter-based exploration; this algorithm samples directly in the parameter space. Unlike previous methods based on natural gradients, our algorithm calculates the natural policy gradient using the inverse of the exact Fisher information matrix. The computational cost of this algorithm is equal to that of conventional policy gradients whereas previous natural policy gradient methods have a prohibitive computational cost. Experimental results show that the proposed method outperforms several policy gradient methods.