text:   without Sacrifices",5,"['Want your robot to explore intelligently? We study how to learn to explore &amp; introduce a *efficient* meta-learning method that can lead to optimal exploration.\n\nPaper: <LINK>\nw Evan Liu, Raghunathan, Liang @StanfordAILab\n\nThread👇🏼(1/5)\n<LINK>', 'Prior meta-RL methods either (a) optimize exploration &amp; execution end-to-end w.r.t. reward (e.g. RL^2, VariBAD), or (b) leverage principled but suboptimal strategies (e.g. PEARL).\n\nThe former is particularly hard, as it leads to a chicken-and-egg optimization problem.\n(2/5) https://t.co/0fge7AGySO', 'Turns out you can break this coupling by training a task-conditioned execution policy, and training the exploration policy to recover task-relevant information. \n\nThis is consistent with the end-to-end objective *and* substantially more efficient!\n(3/5) https://t.co/b48gB1VgPW', 'With this approach, DREAM can learn an exploration strategy that navigates a 3D environment from pixels to go “read” a sign that carries info about the task. (and then execute the task using that info)\n(4/5) https://t.co/4teggu5vXP', 'In comparison with state-of-the-art meta-RL methods, this approach can better scale to challenging meta-RL problems such as 3D visual object navigation.\n\nSee the paper for more experiments &amp; theoretical analysis.\n(5/5) https://t.co/HeC6C3JSz2']",20,08,1221