Learning with Deictic Representation
Most reinforcement learning methods operate on propositional representations of the world state. Such representations are often intractably large and generalize poorly. Using a deictic representation is believed to be a viable alternative: they promise generalization while allowing the use of e...
| Main Authors: | , , , |
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| Language: | en_US |
| Published: |
2004
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| Subjects: | |
| Online Access: | http://hdl.handle.net/1721.1/6685 |