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      "text": "Planning with model-based RL requires breaking the task at hand over multiple small steps, and planning at each one. This is challenging over long time horizons: (a) the longer the horizon, the longer modeling errors accumulate, and (b) planning at each state quickly becomes intractable. L3P learns over a sparser set of steps. To do this, L3P clusters intermediate goals that are easily reachable from one another, thereby learning a small number of important landmarks. Landmarks are modeled as nodes, and the edges are weighted by a reachability distance between the landmarks. Finally, L3P uses graph search to compute the shortest path to the goal.",
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