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- W4226191630 abstract "We present CURLKG: CURL [1] enhanced with Knowledge Graph method. Inspired by knowledge graph embedding, environment states are represented as entities and actions are relations between knowledge entities in CURLKG. This knowledge embedding auxiliary task gives “similarity” a more precise measure compared with previous contrastive image representation learning models. Experiments are conducted in Deepmind Controal Suit environment and shows that CURLKG outperforms original CURL with a 1.29x performance gain at 100K environment steps. The main advantage of our model is simplicity and generality. CURLKG can be combined with any reinforcement learning model and no specialized network structure is used. CURLKG is the first pixel representation method inspired with the idea of knowledge embedding which is used as a supplementary constraint to contrastive learning." @default.
- W4226191630 created "2022-05-05" @default.
- W4226191630 creator A5050253102 @default.
- W4226191630 date "2021-12-01" @default.
- W4226191630 modified "2023-09-29" @default.
- W4226191630 title "Contrastive Visual Representation Learning Enhanced with Knowledge Embedding for Reinforcement Learning" @default.
- W4226191630 doi "https://doi.org/10.1109/cecit53797.2021.00079" @default.
- W4226191630 hasPublicationYear "2021" @default.
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