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| Citation key | Boehmer2013b |
|---|---|
| Author | Böhmer, W. and Obermayer, K. |
| Year | 2013 |
| Journal | ICRA Workshop on Autonomous Learning |
| Abstract | Autonomous agents do not always have access to the amount of samples machine learning methods require. Structural assumptions like factored MDP allow to generalize experiences beyond traditional metrics to entirely new situations. This paper introduces a novel framework to exploit such knowledge for approximated policy iteration. At the heart of the framework a novel factored approximate planning algorithm is derived. The algorithm requires no real observations and optimizes control for given linear reward and transition models. It is empirically compared with least squares policy iteration in a continuous navigation task. Computational leverage in constructing the linear models without observing the entire state space and in representation of the solution are discussed as well. |
| Bibtex Type of Publication | Selected:reinforcement |
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