Official code base for IQ-Learn: Inverse soft-Q Learning for Imitation, NeurIPS '21 Spotlight

Update: IQ-Learn was recently used to create the best AI agent for playing Minecraft. Placing #1 in NeurIPS MineRL Basalt Challenge using only human demos (Overall Leaderboard Rank #2)

[Project Page]

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).

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.

@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}
}

✅ 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

To install and use IQ-Learn check the instructions provided in the iq_learn folder.

Reaching human-level performance on Atari with pure imitation:

Recovering environment rewards on GridWorld:

Please feel free to email us if you have any questions.

Div Garg ([email protected])