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SAC

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

SAC appears in the code Syntology harvested for 8 papers, as 20 distinct code bodies found in 20 places (a place is one code body under one paper). At least one of them ran in 3 of the papers; 1 of the code bodies carries 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 SAC 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 4 of the 20 distinct code bodies named SAC; 16 are 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
4ran
16unverified
1fingerprinted

Licence is a property of each copy, so it is counted per place: 9 of the 20 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

8 papers shown of 8, newest first; 20 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. 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
MIGC: Multi-Instance Generation Controller for Text-to-Image Synthesis 8 Feb 2024 limuloo/migc/migc/migc_arch.py b94c1c2a13728953 ran fingerprinted licence not identified · pointer only
REvolveR: Continuous Evolutionary Models for Robot-to-robot Policy Transfer 10 Feb 2022 xingyul/revolver/gym/SAC.py b338db7b6d988868 unverified GPL-2.0 (copyleft) · pointer only
Iterative Filter Adaptive Network for Single Image Defocus Deblurring 31 Aug 2021 codeslake/IFAN/models/archs/IFAN.py fbf1f45bff48a98f unverified AGPL-3.0 (copyleft) · pointer only
Self-supervised Augmentation Consistency for Adapting Semantic Segmentation 30 Apr 2021 visinf/da-sac/models/sac.py c75f3289ccfc5cc2 unverified Apache-2.0 (permissive)
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler 17 Oct 2020 NeurIPS2020AnonymousSubmission/mesa/mesa.py 2eade89a5f8f0440 unverified MIT (permissive)
Reinforcement Learning with Augmented Data 30 Apr 2020 KarlXing/RL-Visual-Continuous-Control/src/agent/rad.py bf3db7327a139e28 ran MIT (permissive)
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 4 Jan 2018 ShawK91/Evolutionary-Reinforcement-Learning/algos/sac.py 217cd7f6f86864da ran no licence file found · pointer only
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 4 Jan 2018 SaminYeasar/off_policy_ac/SAC/SAC.py 43d63d9b3b6240a1 ran no licence file found · pointer only
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 4 Jan 2018 kdally/fault-tolerant-flight-control-drl/fault_tolerant_flight_control_drl/agent/sac.py 569d921eb9b51536 unverified MIT (permissive)
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 4 Jan 2018 baturaysaglam/la3p/Code/SAC/SAC.py 0edd4b0f90be8f84 unverified MIT (permissive)
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 4 Jan 2018 yhisaki/average-reward-drl/average_reward_drl/algorithms/sac.py 4849ba61ae47dd70 unverified no licence file found · pointer only
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 4 Jan 2018 ku2482/discor.pytorch/discor/algorithm/sac.py 348b1924f106de50 unverified MIT (permissive)
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 4 Jan 2018 h-aboutalebi/SparceReward/engine/algorithms/SAC/sac.py 9e2e2c85eabbd88f unverified no licence file found · pointer only
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 4 Jan 2018 sunfex/weighted-sac/sac.py 1cb5a8aa4ee6cb43 unverified MIT (permissive)
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 4 Jan 2018 hyunin-lee/ForecasterSAC/sac.py 9efbd1e63c6644e5 unverified MIT (permissive)
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 4 Jan 2018 garyzyr001/rethinking-airl/airl/algo/sac.py 6af70c8a2239d7c5 unverified no licence file found · pointer only
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 4 Jan 2018 MarsEleven/car_racer_RL/sac/sac.py fa07dbb92f6c222b unverified no licence file found · pointer only
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 4 Jan 2018 ku2482/gail-airl-ppo.pytorch/gail_airl_ppo/algo/sac.py 104ef2d3e37baa21 unverified MIT (permissive)
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 4 Jan 2018 pranz24/pytorch-soft-actor-critic/sac.py d77f61cb28da8383 unverified MIT (permissive)
Continuous control with deep reinforcement learning 9 Sep 2015 Souphis/mobile_robot_rl/mobile_robot_rl/agents/sac.py 1be47476efdc31c1 unverified 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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