Active Policy Iteration: Efficient Exploration through Active Learning for Value Function Approximation in Reinforcement Learning - researchr publication references

researchr

You are not signed in
Sign in
Sign up

Takayuki Akiyama, Hirotaka Hachiya, Masashi Sugiyama. Active Policy Iteration: Efficient Exploration through Active Learning for Value Function Approximation in Reinforcement Learning. In Craig Boutilier, editor, IJCAI 2009, Proceedings of the 21st International Joint Conference on Artificial Intelligence, Pasadena, California, USA, July 11-17, 2009. pages 980-985, 2009. [doi]

No references recorded for this publication.

No citations of this publication recorded.

runs on WebDSL