Learning sparse relational transition models
© 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved. We present a representation for describing transition models in complex uncertain domains using relational rules. For any action, a rule selects a set of relevant objects and computes a distribution over prop...
| Hlavní autoři: | , , , , |
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| Médium: | Článek |
| Jazyk: | English |
| Vydáno: |
2021
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| On-line přístup: | https://hdl.handle.net/1721.1/132315 |
| Shrnutí: | © 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved. We present a representation for describing transition models in complex uncertain domains using relational rules. For any action, a rule selects a set of relevant objects and computes a distribution over properties of just those objects in the resulting state given their properties in the previous state. An iterative greedy algorithm is used to construct a set of deictic references that determine which objects are relevant in any given state. Feed-forward neural networks are used to learn the transition distribution on the relevant objects' properties. This strategy is demonstrated to be both more versatile and more sample efficient than learning a monolithic transition model in a simulated domain in which a robot pushes stacks of objects on a cluttered table. |
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