Papers › IQ-Learn: Inverse soft-Q Learning for Imitation
IQ-Learn: Inverse soft-Q Learning for Imitation
Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, Matthieu Geist, Stefano Ermon
In many sequential decision-making problems (e.g., robotics control, game playing, sequential prediction), human or expert data is available containing useful information about the task. However, imitation learning (IL) from a small amount of expert data can be challenging in high-dimensional environments with complex dynamics. Behavioral cloning is a simple method that is widely used due to its simplicity of implementation and stable convergence but doesn't utilize any information involving the environment's dynamics. Many existing methods that exploit dynamics information are difficult to train in practice due to an adversarial optimization process over reward and policy approximators or biased, high variance gradient estimators. We introduce a method for dynamics-aware IL which avoids adversarial training by learning a single Q-function, implicitly representing both reward and policy. On standard benchmarks, the implicitly learned rewards show a high positive correlation with the ground-truth rewards, illustrating our method can also be used for inverse reinforcement learning (IRL). Our method, Inverse soft-Q learning (IQ-Learn) obtains state-of-the-art results in offline and online imitation learning settings, significantly outperforming existing methods both in the number of required environment interactions and scalability in high-dimensional spaces, often by more than 3x.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Atari Games | Atari 2600 Beam Rider | IQ-Learn | Return | 3025 | #49 of 49 | Archive leaderboard | report |
| Atari Games | Atari 2600 Q*Bert | IQ-Learn | Return | 12940 | #57 of 57 | Archive leaderboard | report |
| Atari Games | Atari 2600 Seaquest | IQ-Learn | Return | 2349 | #57 of 57 | Archive leaderboard | report |
| Atari Games | Atari 2600 Space Invaders | IQ-Learn | Return | 507 | #55 of 55 | Archive leaderboard | report |
| MuJoCo Games | Ant | IQ-Learn | Average Return | 4362.9 | #1 of 3 | Archive leaderboard | report |
| MuJoCo Games | Humanoid-v2 | IQ-Learn | Return | 5227.1 | #1 of 1 | Archive leaderboard | report |
| MuJoCo Games | Walker2d | IQ-Learn | Mean | 5134 | #1 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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