Feature Markov Decision Processes
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Description
General purpose intelligent learning agents cycle through (complex,non-MDP) sequences of observations, actions, and rewards. On the other hand, reinforcement learning is well-developed for small finite state Markov Decision Processes (MDPs). So far it is
| Collections | ANU Research Publications |
|---|---|
| Date published: | 2009 |
| Type: | Conference paper |
| URI: | http://hdl.handle.net/1885/58168 |
| Source: | Advances in Intelligent Systems Research: Proceedings of the 2nd Conference on Artificial General Intelligence (AGI 2009) |
| DOI: | 10.2991/agi.2009.30 |
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| File | Description | Size | Format | Image |
|---|---|---|---|---|
| 01_Hutter_Feature_Markov_Decision_2009.pdf | 420.46 kB | Adobe PDF | Request a copy | |
| 02_Hutter_Feature_Markov_Decision_2009.pdf | 37.49 kB | Adobe PDF | Request a copy | |
| 03_Hutter_Feature_Markov_Decision_2009.pdf | 55.8 kB | Adobe PDF | Request a copy | |
| 04_Hutter_Feature_Markov_Decision_2009.pdf | 55.32 kB | Adobe PDF | Request a copy |
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