Papers › IQ-Learn: Inverse soft-Q Learning for Imitation

IQ-Learn: Inverse soft-Q Learning for Imitation

23 Jun 2021NeurIPS 2021 12arXiv:2106.12142archive 2025-07-28

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.

PaperPDFConference PDFCodeCode Syntology ran

In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.

For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="2106.12142")

Code

Syntology Ran 2 of 3 code samples harvested from 1 repository linked to this paper; 1 has no recorded run. Of those that ran: 2 ran · our draft was wrong.

By repository: community (archive-listed): 3 samples from 1 repository, 2 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.

Div99/IQ-Learn officialmentioned in papermentioned on GitHubpytorch report
MilkSilk/masters_thesis mentioned on GitHub report
edmundmills/basalt-competition mentioned on GitHubpytorch report
google-deepmind/csil mentioned on GitHubjaxApache-2.0 report
robfiras/ls-iq mentioned on GitHubpytorchMIT report

Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.

Code Syntology ran Syntology

3 samples harvested; 2 ran; 0 honoured the contract we drafted; 1 has no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.

2ran · our draft was wrong
1unverified

Licence: 3 of the 3 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.

Harvested from edmundmills/basalt-competition. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.

Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.

Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.

cat_states edmundmills/basalt-competition/algorithms/loss_functions/iqlearn.py community (archive-listed) ran · our draft was wrong licence not identified · pointer only · d188609497f5bd0e · report
cat_transitions edmundmills/basalt-competition/algorithms/loss_functions/iqlearn.py community (archive-listed) ran · our draft was wrong licence not identified · pointer only · a14c0c0e85392744 · report
IQLearnLoss edmundmills/basalt-competition/algorithms/loss_functions/iqlearn.py community (archive-listed) unverified licence not identified · pointer only · b9c72ed3c904ebc9 · report

Tasks

Atari GamesContinuous ControlDecision MakingImitation LearningMuJoCo GamesQ-LearningSequential Decision Making

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
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

IQ-Learn

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections