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{
            
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  "access_rights": "",
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  "access_rights": "",
            
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  "author": "Wenjie Shi",
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  "author": "Wenjie Shi",
            
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  "author_email": "",
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  "author_email": "",
            
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  "citation": [
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  "citation": [
            
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    "https://doi.org/10.48550/arXiv.2003.07069"
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    "https://doi.org/10.48550/arXiv.2003.07069"
            
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  ],
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
            
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  "defined_in": "https://doi.org/10.48550/arXiv.2310.17173",
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  "defined_in": "https://doi.org/10.48550/arXiv.2310.17173",
            
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  "doi": "10.57702/z2n4g3e0",
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  "doi": "10.57702/z2n4g3e0",
            
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  "doi_date_published": null,
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  "doi_date_published": "2024-12-16",
            
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  "doi_publisher": "TIB",
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  "doi_publisher": "TIB",
            
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  "domain": "https://service.tib.eu/ldmservice",
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  "domain": "https://service.tib.eu/ldmservice",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": "Gao Huang",
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      "extra_author": "Gao Huang",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Shiji Song",
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      "extra_author": "Shiji Song",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Zhuoyuan Wang",
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      "extra_author": "Zhuoyuan Wang",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Tingyu Lin",
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      "extra_author": "Tingyu Lin",
            
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      "orcid": ""
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": "Cheng Wu",
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      "extra_author": "Cheng Wu",
            
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      "orcid": ""
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      "orcid": ""
            
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    }
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      "name": "attention-mechanisms",
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      "name": "attention-mechanisms",
            
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      "title": "Attention Mechanisms"
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      "title": "Attention Mechanisms"
            
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    },
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      "name": "deep-learning",
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      "name": "deep-learning",
            
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      "title": "Deep Learning"
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      "title": "Deep Learning"
            
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    {
            
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      "description": "",
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      "description": "",
            
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      "display_name": "Gaming",
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      "display_name": "Gaming",
            
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      "name": "gaming",
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      "name": "gaming",
            
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      "title": "Gaming"
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      "title": "Gaming"
            
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    {
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    {
            
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      "description": "",
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      "description": "",
            
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      "display_name": "Reinforcement Learning",
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      "image_display_url": "",
            
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      "name": "reinforcement-learning",
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      "name": "reinforcement-learning",
            
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      "title": "Reinforcement Learning"
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      "title": "Reinforcement Learning"
            
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    }
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    }
            
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  "landing_page": "https://github.com/shiwj16/SSINet",
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  "landing_page": "https://github.com/shiwj16/SSINet",
            
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  "license_title": null,
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  "link_orkg": "",
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  "link_orkg": "",
            
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  "metadata_created": "2024-12-16T17:52:51.359003",
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  "metadata_created": "2024-12-16T17:52:51.359003",
            
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  "metadata_modified": "2024-12-16T17:52:51.359010",
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  "metadata_modified": "2024-12-16T17:52:51.793998",
            
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  "name": "atari-2600-games",
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  "name": "atari-2600-games",
            
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  "notes": "The dataset used in this paper is a collection of 
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  "notes": "The dataset used in this paper is a collection of 
            
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state-action pairs generated by a pre-trained RL agent, used to train 
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state-action pairs generated by a pre-trained RL agent, used to train 
            
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a self-supervised interpretable network (SSINet) to produce attention 
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a self-supervised interpretable network (SSINet) to produce attention 
            
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masks for explaining the agent's decisions.",
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masks for explaining the agent's decisions.",
            
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    "approval_status": "approved",
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    "approval_status": "approved",
            
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    "created": "2024-11-25T12:11:38.292601",
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    "state": "active",
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  "private": false,
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      "created": "2024-12-16T18:25:31",
            
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      "description": "The json representation of the dataset with its 
            
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distributions based on DCAT.",
            
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      "format": "JSON",
            
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      "hash": "",
            
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      "id": "ec93554f-29a7-4f16-af32-465f0cf6c3ab",
            
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      "display_name": "Atari 2600",
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      "name": "Atari 2600",
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    {
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      "display_name": "Atari 2600 games",
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      "name": "Attention masks",
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      "state": "active",
            
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    },
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    },
            
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      "state": "active",
            
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      "vocabulary_id": null
            
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    },
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    },
            
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    {
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    {
            
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      "display_name": "Games",
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      "display_name": "Games",
            
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      "vocabulary_id": null
            
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    },
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    },
            
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      "name": "Gym",
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      "name": "Gym",
            
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      "state": "active",
            
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      "vocabulary_id": null
            
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    },
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    },
            
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    {
            
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      "display_name": "Reinforcement Learning",
            
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      "id": "0bd14238-9c5d-4905-8165-c7e5a0c0884c",
            
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      "name": "Reinforcement Learning",
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      "name": "Reinforcement Learning",
            
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      "state": "active",
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      "state": "active",
            
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      "vocabulary_id": null
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      "vocabulary_id": null
            
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    },
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    },
            
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    {
            
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      "display_name": "Self-supervised learning",
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      "display_name": "Self-supervised learning",
            
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      "name": "Self-supervised learning",
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      "name": "Self-supervised learning",
            
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      "state": "active",
            
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      "vocabulary_id": null
            
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    }
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    }
            
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  ],
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  ],
            
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  "title": "Atari 2600 games",
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  "title": "Atari 2600 games",
            
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  "type": "dataset",
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  "type": "dataset",
            
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  "version": ""
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  "version": ""
            
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}
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}