Inverse Q-Learning (IQ-Learn)

[Project Page] [Blog Post] Official code base for IQ-Learn: Inverse soft-Q Learning for Imitation, NeurIPS '21 Spotlight

IQ-Learn is an simple, stable & data-efficient algorithm that's a drop-in replacement to methods like Behavior Cloning and GAIL, to boost your imitation learning pipelines!

Update: IQ-Learn was recently used to create the best AI agent for playing Minecraft. Placing #1 in NeurIPS MineRL Basalt Challenge using only recorded human player demos. (IQ-Learn also competed with methods that use human-in-the-loop interactions and surprisingly still achieved Overall Rank #2)

We introduce Inverse Q-Learning (IQ-Learn), a state-of-the-art novel 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.

Inverse Q-Learning is theoretically equivalent to Inverse Reinforcement learning, i.e. learning rewards from expert data. However, it is much more powerful in practice. It admits very simple non-adverserial training and works on complete offline IL settings (without any access to the environment), greatly exceeding Behavior Cloning.

IQ-Learn is the successor to Adversarial Imitation Learning methods like GAIL (coming from the same lab).
It extends the theoretical framework for Inverse RL to non-adverserial and scalable learning, for the first-time showing guaranteed convergence.

Citation

@inproceedings{garg2021iqlearn,
title={IQ-Learn: Inverse soft-Q Learning for Imitation},
author={Divyansh Garg and Shuvam Chakraborty and Chris Cundy and Jiaming Song and Stefano Ermon},
booktitle={Thirty-Fifth Conference on Neural Information Processing Systems},
year={2021},
url={https://openreview.net/forum?id=Aeo-xqtb5p}
}

Key Advantages

✅ Drop-in replacement to Behavior Cloning
✅ Non-adverserial online IL (Successor to GAIL & AIRL)
✅ Simple to implement
✅ Performant with very sparse data (single expert demo)
✅ Scales to Complex Image Envs (SOTA on Atari and playing Minecraft)
✅ Recover rewards from envs