Inverse Q-Learning (IQ-Learn) is a a simple, stable & data-efficient framework for Imitation Learning (IL), that directly learns soft Q-functions from expert data. IQ-Learn enables non-adverserial imitation learning, working on both offline and online IL settings. It is performant even with very sparse expert data, and scales to complex image-based environments, surpassing prior methods by more than 3x.
It is very simple to implement requiring ~15 lines of code on top of existing RL methods.
Paper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Imitation Learning | 3 | 42.86% |
Reinforcement Learning (RL) | 1 | 14.29% |
Atari Games | 1 | 14.29% |
Continuous Control | 1 | 14.29% |
Decision Making | 1 | 14.29% |
Component | Type |
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🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |