Imitation Learning Methods
Inverse Q-Learning
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.
Papers
| Paper | Code | Results | Date | Stars |
|---|
Tasks
| Task | Papers | Share |
|---|---|---|
| Imitation Learning | 3 | 20.00% |
| Decision Making | 2 | 13.33% |
| Sequential Decision Making | 2 | 13.33% |
| Autonomous Driving | 1 | 6.67% |
| Clustering | 1 | 6.67% |
| regression | 1 | 6.67% |
| Self-Driving Cars | 1 | 6.67% |
| Reinforcement Learning | 1 | 6.67% |
| Reinforcement Learning (RL) | 1 | 6.67% |
Usage Over Time
Components
| Component | Type |
|
|---|---|---|
| 🤖 No Components Found | You can add them if they exist; e.g. Mask R-CNN uses RoIAlign |
