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  "text": "This might be my favorite paper of the year🤯\n\nRich Sutton claims that current RL methods won't get us to continual learning because they don't compound upon previous knowledge, every rollout starts from scratch.\n\nResearchers in Switzerland introduce Meta-RL which might crack that code. Optimize across episodes with a meta-learning objective, which then incentivizes agents to explore first and then exploit. And then reflect upon previous failures for future agent runs.\n\nIncredible results and incredible read of a paper overall.\nAuthors: @YulunJiang @LiangzeJ @DamienTeney @Michael_D_Moor @mariabrbic",
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