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Feb, 2020
用逆强化学习改写历史：后见推断对政策改进的影响
Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement
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Benjamin Eysenbach, Xinyang Geng, Sergey Levine, Ruslan Salakhutdinov
TL;DR
本文介绍了逆强化学习（inverse RL），采用逆强化学习方法来实现目标重标记技术（goal-relabeling techniques），并证实在多任务设置下，包括目标达成、具有离散奖励集合和线性奖励函数的领域中，使用逆强化学习加速了学习过程。
Abstract
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 effi
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