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get_average_returns

Syntologyentry name in harvested coderead from the graph 2026-09-24

get_average_returns appears in the code Syntology harvested for 27 papers, as 3 distinct code bodies found in 27 places (a place is one code body under one paper). At least one of them ran in 26 of the papers; 0 of the code bodies carry a behaviour fingerprint.

What this page is not. Routines are grouped here by the exact string of their function or class name. Nothing asserts that two samples named get_average_returns do the same thing, share code, or are comparable; the name is a string, not an identity. Behaviour outputs (what a fingerprinted sample returned on the shared battery) are not in this export and are not shown here; the graph at syntology.ai holds them. "Ran" means executed on a synthesized fixture, not that the code is correct or reproduces a paper.

Samples Syntology

Syntology ran 2 of the 3 distinct code bodies named get_average_returns; 1 is unverified. One tile per status, in the site's fixed vocabulary, each code body counted once:

0ran · honoured contract
0ran · violated contract
0ran · our draft was wrong
0ran · fixture could not drive it
2ran
1unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 5 of the 27 places are pointer only (Syntology does not serve that copy's text). This site shows no code text for any sample; every row below links to the file in its repository where the record names one.

“Ran” means the sample executed on a synthesized input; it does not mean the output is correct. “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, and those samples did run. The ran count above is every status except unverified, the same rule as each paper page.

Papers

27 papers shown of 27, newest first; 27 places in the table. A paper with no recorded date is placed by the month its arXiv id encodes, shown in the Date column as YYYY-MM (from id). One row per place: a paper whose repository defines the name more than once appears more than once, and the same code body held for several papers appears once under each, with the same status. Titles and dates are the archive's archive 2025-07-28 for papers in the archive, and the graph's for 2 papers added by Syntology; 1 papers have no page here and are shown by arXiv id only. Status and fingerprint are Syntology's record of each code body; licence is recorded for each place. The File cell ends with the code body's 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.

PaperDateFileStatus SyntologyLicence
Diffusion Policy with Behavioral Advantage Correction for Offline Reinforcement Learning added by Syntology 2026-08 (from id) aviralkumar2907/CQL/d4rl/rlkit/core/eval_util.py a5d10a0b704c8db7 ran no licence file found · pointer only
PENG'S Q(λ) FOR CONSERVATIVE VALUE ESTIMATION IN OFFLINE REINFORCEMENT LEARNING added by Syntology 2026-05 (from id) hyeon1996/EPQ/rlkit/core/eval_util.py a5d10a0b704c8db7 ran no licence file found · pointer only
arXiv:2507.08387 2025-07 (from id) shlee94/Off2OnRL/rlkit/rlkit/core/eval_util.py a5d10a0b704c8db7 ran no licence file found · pointer only
Surrogate Learning in Meta-Black-Box Optimization: A Preliminary Study 23 Mar 2025 gmc-drl/surr-rlde/logger.py 5c8c72eaea2e2fcd unverified BSD-2-Clause (permissive)
PRISM: A Robust Framework for Skill-based Meta-Reinforcement Learning with Noisy Demonstrations 6 Feb 2025 katerakelly/oyster/rlkit/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)
Adaptive $Q$-Aid for Conditional Supervised Learning in Offline Reinforcement Learning 3 Feb 2024 rail-berkeley/rlkit/rlkit/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)
DiffAIL: Diffusion Adversarial Imitation Learning 11 Dec 2023 ml-group-sdu/diffail/rlkit/core/eval_util.py 44ccd48c6687e39b ran no licence file found · pointer only
Accelerating Exploration with Unlabeled Prior Data 9 Nov 2023 avisingh599/cog/rlkit/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)
Context Shift Reduction for Offline Meta-Reinforcement Learning 7 Nov 2023 moreanp/csro/rlkit/core/eval_util.py a5d10a0b704c8db7 ran no licence file found · pointer only
Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skills 30 Oct 2023 seongun-kim/vcrl/vcrl/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)
PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks 12 Jul 2023 IanChar/GPIDE/rlkit/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)
Stabilizing Contrastive RL: Techniques for Robotic Goal Reaching from Offline Data 6 Jun 2023 chongyi-zheng/stable_contrastive_rl/rlkit/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)
Adaptive and Explainable Deployment of Navigation Skills via Hierarchical Deep Reinforcement Learning 2023-05 (from id) leekwoon/hrl-nav/src/rlkit/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)
On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations 28 Dec 2022 conglu1997/nppac/rlkit/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)
S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning 30 Sep 2022 dsshim0125/s2p/rlkit/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)
Mildly Conservative Q-Learning for Offline Reinforcement Learning 9 Jun 2022 vitchyr/rlkit/rlkit/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)
How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression 7 Jun 2022 jasonma2016/gofar/envs/env_util.py a5d10a0b704c8db7 ran MIT (permissive)
FedFormer: Contextual Federation with Attention in Reinforcement Learning 27 May 2022 liamhebert/FedFormer/rlkit/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)
Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation Tasks 7 Oct 2021 UT-Austin-RPL/maple/maple/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)
What Can I Do Here? Learning New Skills by Imagining Visual Affordances 1 Jun 2021 patrickhaoy/ptp/rlkit/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)
Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning 19 Apr 2021 JieRen98/rlkit-pmoe/rlkit/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)
Hyperparameter Auto-tuning in Self-Supervised Robotic Learning 16 Oct 2020 birlrobotics/rlkit_autotune/rlkit/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)
Off-Dynamics Reinforcement Learning: Training for Transfer with Domain Classifiers 24 Jun 2020 shreyasc-13/off_domain_rlkit/rlkit/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)
Conservative Q-Learning for Offline Reinforcement Learning 8 Jun 2020 ikostrikov/cql-results/d4rl/rlkit/core/eval_util.py a5d10a0b704c8db7 ran Apache-2.0 (permissive)
Towards Practical Multi-Object Manipulation using Relational Reinforcement Learning 23 Dec 2019 richardrl/rlkit-relational/rlkit/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)
Contextual Imagined Goals for Self-Supervised Robotic Learning 23 Oct 2019 anair13/rlkit/rlkit/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)
Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables 19 Mar 2019 lujiayou123/Off-Policy-Meta-Reinforcement-Learning-via-Unsupervised-Domain-Translation/rlkit/core/eval_util.py a5d10a0b704c8db7 ran MIT (permissive)

This site shows no code text; each File cell links to the file on GitHub at the repository's current default branch, which may have changed since the 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 cell for the reason. Per-sample records for a paper are on its paper page under "Code Syntology ran".

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