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  "author": "Jianlan Luo",
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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      "extra_author": "Perry Dong",
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      "extra_author": "Perry Dong",
            
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    },
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    {
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      "extra_author": "Jeffrey Wu",
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      "orcid": ""
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    },
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    {
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      "extra_author": "Aviral Kumar",
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    },
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    {
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      "extra_author": "Xinyang Geng",
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      "extra_author": "Xinyang Geng",
            
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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": "Sergey Levine",
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      "extra_author": "Sergey Levine",
            
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      "orcid": ""
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-quantized-offline-reinforcement-learning-for-robotic-skill-learning",
            
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  "notes": "Offline reinforcement learning (RL) paradigm provides a 
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  "notes": "Offline reinforcement learning (RL) paradigm provides a 
            
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general recipe to convert static behavior datasets into policies that 
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general recipe to convert static behavior datasets into policies that 
            
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can perform better than the policy that collected the data.",
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      "display_name": "Offline RL",
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  "title": "Action-Quantized Offline Reinforcement Learning for 
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Robotic Skill Learning",
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Robotic Skill Learning",
            
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}