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DQN

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

DQN appears in the code Syntology harvested for 8 papers, as 23 distinct code bodies found in 23 places (a place is one code body under one paper). At least one of them ran in 4 of the papers; 3 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 DQN 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 13 of the 23 distinct code bodies named DQN; 10 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
13ran
10unverified
3fingerprinted

Licence is a property of each copy, so it is counted per place: 14 of the 23 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; 23 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 1 papers added by Syntology. 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
ReLaTS: a Reinforcement Learning-based method for dynamically determining the coupling Time Step in multi-scale simulations of self-gravitating systems added by Syntology 2026-06 (from id) veronicasaz/RL_bridgedCluster/TrainRL.py a2ba14c94d554b58 ran · metamorphic tier: invariant fingerprinted no licence file found · pointer only
Automated Proof of Polynomial Inequalities via Reinforcement Learning 9 Mar 2025 blliu6/APPIRL/proof/dqn.py 481aca1daf0d80ec unverified no licence file found · pointer only
Weakly Coupled Deep Q-Networks 28 Oct 2023 ibrahim-elshar/WCDQN_NeurIPS/src/Inv_control/WCDQN.py 10469ffead06c867 unverified no licence file found · pointer only
Adapting to Reward Progressivity via Spectral Reinforcement Learning 29 Apr 2021 mchldann/SpectralDQN/agent/dqn.py 957ce6e2d60df983 ran · metamorphic tier: deterministic GPL-2.0 (copyleft) · pointer only
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching 16 Dec 2020 thinklab-sjtu/rgm/dqn_model_r.py 29cec35c08a131ba unverified no licence file found · pointer only
LaProp: Separating Momentum and Adaptivity in Adam 12 Feb 2020 Z-T-WANG/LaProp-Optimizer/rainbow/model.py ed2dc93a9723ec69 unverified MIT (permissive)
Deep Reinforcement Learning with Double Q-learning 22 Sep 2015 xgfelicia/Reinforcement-Learning/Cartpole/double-dqn.py 8002739ef399b9f8 ran · metamorphic tier: deterministic fingerprinted no licence file found · pointer only
Deep Reinforcement Learning with Double Q-learning 22 Sep 2015 kmdanielduan/DQN_Family_PyTorch/agent.py 249aaac189a4c179 ran · metamorphic tier: deterministic fingerprinted no licence file found · pointer only
Deep Reinforcement Learning with Double Q-learning 22 Sep 2015 hamishs/JAX-RL/src/jax_rl/algorithms/dqn.py 0493df484647c5af ran MIT (permissive)
Deep Reinforcement Learning with Double Q-learning 22 Sep 2015 yzheng51/rl-dino-run/agent.py 4cd993bb1241d36c ran MIT (permissive)
Deep Reinforcement Learning with Double Q-learning 22 Sep 2015 OscarHuangWind/Preference-Guided-DQN-Atari/DRL.py 5964c193c9302049 unverified MIT (permissive)
Deep Reinforcement Learning with Double Q-learning 22 Sep 2015 ifestus/rl/dqn/dqn.py 2c00e8186282ff76 unverified no licence file found · pointer only
Deep Reinforcement Learning with Double Q-learning 22 Sep 2015 tensorlayer/RLzoo/rlzoo/algorithms/dqn/dqn.py 39cd1cea29282ec1 unverified Apache-2.0 (permissive)
Playing Atari with Deep Reinforcement Learning 19 Dec 2013 bay3s/dqn/src/agents/dqn.py 9c1d06fe674d019d ran no licence file found · pointer only
Playing Atari with Deep Reinforcement Learning 19 Dec 2013 TheFebrin/DeepRL-Pong/models/dqn_model.py a54a3b810f63e7d1 ran MIT (permissive)
Playing Atari with Deep Reinforcement Learning 19 Dec 2013 MaximeVandegar/Papers-in-100-Lines-of-Code/Playing_Atari_with_Deep_Reinforcement_Learning/dqn.py cc31fb79a4c9cfad ran MIT (permissive)
Playing Atari with Deep Reinforcement Learning 19 Dec 2013 daviddcho/supermario/model.py e65d2c13d9aa7641 ran no licence file found · pointer only
Playing Atari with Deep Reinforcement Learning 19 Dec 2013 gordicaleksa/pytorch-learn-reinforcement-learning/models/definitions/DQN.py 51cf7eff17d1081c ran MIT (permissive)
Playing Atari with Deep Reinforcement Learning 19 Dec 2013 K-tang-mkv/baseRLAlgorithm/algorithms/DQN_pytorch_offical/dqn.py 7ac5ee56c6e889b6 ran · metamorphic tier: invariant no licence file found · pointer only
Playing Atari with Deep Reinforcement Learning 19 Dec 2013 vincentpalma/DQN-for-CaRL/dqn_training.py c7a2e9b74e4de596 ran · metamorphic tier: invariant no licence file found · pointer only
Playing Atari with Deep Reinforcement Learning 19 Dec 2013 anita-hu/TF2-RL/DQN/TF2_DQN_Basic.py 7a2a672d94b88c2c unverified MIT (permissive)
Playing Atari with Deep Reinforcement Learning 19 Dec 2013 eublefar/dqn/dqn.py 5114ef5f140eab1f unverified no licence file found · pointer only
Playing Atari with Deep Reinforcement Learning 19 Dec 2013 JonasRSV/DQNTensorflow/dqn.py 01a1660b174e9811 unverified no licence file found · pointer only

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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