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http://dspace.mediu.edu.my:8181/xmlui/handle/1721.1/6685| Title: | Learning with Deictic Representation |
| Keywords: | AI Reinforcement Learning Partial Observability Representations |
| Issue Date: | 9-Oct-2013 |
| Description: | 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 existing reinforcement-learning methods. Yet, there are few experiments on learning with deictic representations reported in the literature. In this paper we explore the effectiveness of two forms of deictic representation and a naive propositional representation in a simple blocks-world domain. We find, empirically, that the deictic representations actually worsen performance. We conclude with a discussion of possible causes of these results and strategies for more effective learning in domains with objects. |
| URI: | http://koha.mediu.edu.my:8181/xmlui/handle/1721 |
| Other Identifiers: | AIM-2002-006 http://hdl.handle.net/1721.1/6685 |
| Appears in Collections: | MIT Items |
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