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Weakly-Supervised Reinforcement Learning for Controllable Behavior
Lisa Lee · Benjamin Eysenbach · Russ Salakhutdinov · Shixiang (Shane) Gu · Chelsea Finn

Thu Dec 10 09:00 PM -- 11:00 PM (PST) @ Poster Session 6 #1832

Reinforcement learning (RL) is a powerful framework for learning to take actions to solve tasks. However, in many settings, an agent must winnow down the inconceivably large space of all possible tasks to the single task that it is currently being asked to solve. Can we instead constrain the space of tasks to those that are semantically meaningful? In this work, we introduce a framework for using weak supervision to automatically disentangle this semantically meaningful subspace of tasks from the enormous space of nonsensical "chaff" tasks. We show that this learned subspace enables efficient exploration and provides a representation that captures distance between states. On a variety of challenging, vision-based continuous control problems, our approach leads to substantial performance gains, particularly as the complexity of the environment grows.

Author Information

Lisa Lee (CMU)
Benjamin Eysenbach (Carnegie Mellon University)
Benjamin Eysenbach

Assistant professor at Princeton working on self-supervised reinforcement learning (scaling, algorithms, theory, and applications).

Russ Salakhutdinov (Carnegie Mellon University)
Shixiang (Shane) Gu (Google Brain)
Chelsea Finn (Stanford)

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