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Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement
Ben Eysenbach · XINYANG GENG · Sergey Levine · Russ Salakhutdinov

Tue Dec 08 06:00 PM -- 06:15 PM (PST) @ Orals & Spotlights: Reinforcement Learning
in Orals & Spotlights: Reinforcement Learning »

Multi-task reinforcement learning (RL) aims to simultaneously learn policies for solving many tasks. Several prior works have found that relabeling past experience with different reward functions can improve sample efficiency. Relabeling methods typically pose the question: if, in hindsight, we assume that our experience was optimal for some task, for what task was it optimal? Inverse RL answers this question. In this paper we show that inverse RL is a principled mechanism for reusing experience across tasks. We use this idea to generalize goal-relabeling techniques from prior work to arbitrary types of reward functions. Our experiments confirm that relabeling data using inverse RL outperforms prior relabeling methods on goal-reaching tasks, and accelerates learning on more general multi-task settings where prior methods are not applicable, such as domains with discrete sets of rewards and those with linear reward functions.

Keywords: Optimization -> Non-Convex Optimization · Theory -> Statistical Physics of Learning

Author Information

Benjamin Eysenbach (Carnegie Mellon University)
XINYANG GENG (UC Berkeley)
Sergey Levine (UC Berkeley)
Russ Salakhutdinov (Carnegie Mellon University)

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