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ReplayMemory

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

ReplayMemory appears in the code Syntology harvested for 11 papers, as 21 distinct code bodies found in 21 places (a place is one code body under one paper). At least one of them ran in 8 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 ReplayMemory 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 21 distinct code bodies named ReplayMemory; 8 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
8unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 9 of the 21 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

11 papers shown of 11, newest first; 21 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 fa3dff8c2ace4d3a unverified no licence file found · pointer only
Fair yet Asymptotically Equal Collaborative Learning 9 Jun 2023 xqlin98/fair-yet-equal-cml/utils/RL_Environment.py d2cebae41fcbd2cd unverified MIT (permissive)
Influencing Long-Term Behavior in Multiagent Reinforcement Learning 7 Mar 2022 dkkim93/further/algorithm/further/agent.py 6cc0afec8e5e5562 ran MIT (permissive)
MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler 17 Oct 2020 NeurIPS2020AnonymousSubmission/mesa/mesa.py c4cf549ba24993c4 ran MIT (permissive)
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor 4 Jan 2018 ajaysub110/rl-pytorch/sac/models/soft_actor_critic.py 995a5d6382ee476d unverified no licence file found · pointer only
Proximal Policy Optimization Algorithms 20 Jul 2017 llSourcell/OpenAI_Five_vs_Dota2_Explained/ppo.py 7539f57b4803c98e ran MIT (permissive)
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments 7 Jun 2017 isp1tze/MAProj/algo/maddpg/maddpg_agent.py 14185602fbb4fa11 ran no licence file found · pointer only
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments 7 Jun 2017 xuehy/pytorch-maddpg/MADDPG.py 535c542a4e41bb65 ran no licence file found · pointer only
Asynchronous Methods for Deep Reinforcement Learning 4 Feb 2016 brett-daley/fast-dqn/fast_dqn/worker.py 6b7e1cbca8de3a03 ran MIT (permissive)
Prioritized Experience Replay 18 Nov 2015 SimonRamstedt/ddpg/replay_memory.py 5b683309331178dc ran · metamorphic tier: deterministic MIT (permissive)
Prioritized Experience Replay 18 Nov 2015 yzheng51/rl-dino-run/memory.py 1707b7c98445e28d ran MIT (permissive)
Prioritized Experience Replay 18 Nov 2015 Clement-Hui/Q-Learning/replaymemory.py 176c97171a30da8b ran no licence file found · pointer only
Prioritized Experience Replay 18 Nov 2015 sunfex/weighted-sac/replay_memory.py d72a76b37a40fe81 unverified MIT (permissive)
Prioritized Experience Replay 18 Nov 2015 eddynelson/dqn/dqn/replay_memory/replay_memory.py 64f1fd20f4b6af5d unverified Apache-2.0 (permissive)
Prioritized Experience Replay 18 Nov 2015 CSCI4850/S20-team3-project/DoubleQ/Prioritized/ReplayMemory.py 8735a7421b0a382d unverified MIT (permissive)
Deep Reinforcement Learning with Double Q-learning 22 Sep 2015 kmdanielduan/DQN_Family_PyTorch/agent.py 1a174f4ed5cef5f4 ran no licence file found · pointer only
Deep Reinforcement Learning with Double Q-learning 22 Sep 2015 OscarHuangWind/Preference-Guided-DQN-Atari/DRL.py 61d763d7c26919c1 ran MIT (permissive)
Deep Reinforcement Learning with Double Q-learning 22 Sep 2015 ssainz/reinforcement_learning_algorithms/non_jupyter/Frozen_Lake_DDQN.py 1d2063c27405ec4e ran no licence file found · pointer only
Deep Reinforcement Learning with Double Q-learning 22 Sep 2015 HussonnoisMaxence/RL_Algorithms/QLearning/D2QN/D2QN.py fd50d6e813cead15 unverified no licence file found · pointer only
Continuous control with deep reinforcement learning 9 Sep 2015 Souphis/mobile_robot_rl/mobile_robot_rl/agents/sac.py 0d87c7101b2953f2 ran MIT (permissive)
Continuous control with deep reinforcement learning 9 Sep 2015 tilkb/thermoai/controller/RL/DDPG.py 9d5ea952455199bc 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".

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