Home › Code › make_env

make_env

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

make_env appears in the code Syntology harvested for 50 papers, as 51 distinct code bodies found in 56 places (a place is one code body under one paper). At least one of them ran in 7 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 make_env 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 8 of the 51 distinct code bodies named make_env; 43 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
1ran · fixture could not drive it
7ran
43unverified
0fingerprinted

Licence is a property of each copy, so it is counted per place: 17 of the 56 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

50 papers shown of 50, newest first; 56 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 6 papers added by Syntology; 2 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
Pooling and Drift in Delayed Bandits added by Syntology 2026-09 (from id) melikabaghi/state-exp3/code/algo.py 3b178984945a0cec unverified MIT (permissive)
Diffusion Policy with Behavioral Advantage Correction for Offline Reinforcement Learning added by Syntology 2026-08 (from id) sfujim/BCQ/discrete_BCQ/utils.py 4410a954fa46ebfb unverified MIT (permissive)
When Does On-Policy Interaction Help? Representational Tradeoffs in Value-Based Imitation Learning added by Syntology 2026-07 (from id) lviano/ovi/gym_code/run_traj_experiment.py 4e536803a3f0eeb0 ran no licence file found · pointer only
MEMENTO: Memory-Guided Memetic Code-as-Policy Evolution added by Syntology 2026-07 (from id) sygkounas/MEMENTO/MEMENTO/inference.py bef19eafa0358309 unverified no licence file found · pointer only
Towards Robust Zero-Shot Reinforcement Learning added by Syntology 2025-10 (from id) seohongpark/HILP/hilp_gcrl/src/d4rl_utils.py b3d75720ab55c2c2 unverified no licence file found · pointer only
AR-VRM: Imitating Human Motions for Visual Robot Manipulation with Analogical Reasoning added by Syntology 2025-08 (from id) idejie/ar/evaluate.py 21fb46132c505c9e unverified no licence file found · pointer only
Enhancing Cooperative Multi-Agent Reinforcement Learning with State Modelling and Adversarial Exploration 8 May 2025 semitable/multiagent-particle-envs/make_env.py e0db348c4be68fc5 unverified MIT recorded; this copy not marked cleared · pointer only
Beyond Any-Shot Adaptation: Predicting Optimization Outcome for Robustness Gains without Extra Pay 19 Jan 2025 thu-rllab/mpts/MetaRL/sampler.py 4246e3602f23a27a unverified MIT (permissive)
Bidirectional Decoding: Improving Action Chunking via Guided Test-Time Sampling 30 Aug 2024 jubayer-hamid/bid_lerobot/lerobot/common/envs/factory.py a46ac2680ddd2dbe unverified Apache-2.0 (permissive)
Craftium: An Extensible Framework for Creating Reinforcement Learning Environments 4 Jul 2024 mikelma/craftium/cleanrl_ppo_lstm_train.py 47d8fe4d97b14b66 ran licence not identified · pointer only
Craftium: An Extensible Framework for Creating Reinforcement Learning Environments 4 Jul 2024 mikelma/craftium/cleanrl_ppo_train.py 362e38152eaf2597 ran licence not identified · pointer only
No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO 1 May 2024 claire-labo/no-representation-no-trust/src/cleanrl/ppo_mujoco_original.py 06ab2be7a18cea95 ran MIT (permissive)
No Representation, No Trust: Connecting Representation, Collapse, and Trust Issues in PPO 1 May 2024 claire-labo/no-representation-no-trust/src/cleanrl/ppo_mujoco_torchrl.py 6bdd010a8786120c ran MIT (permissive)
Foundation Policies with Hilbert Representations 23 Feb 2024 seohongpark/hilp/hilp_gcrl/src/d4rl_utils.py b3d75720ab55c2c2 unverified no licence file found · pointer only
Q-Star Meets Scalable Posterior Sampling: Bridging Theory and Practice via HyperAgent 5 Feb 2024 szrlee/hyperagent/hyperagent/env/utils.py 7987a0deae85f58b ran MIT (permissive)
Averaging $n$-step Returns Reduces Variance in Reinforcement Learning 6 Feb 2024 brett-daley/averaging-nstep-returns/run_ppo.py 06ab2be7a18cea95 ran MIT (permissive)
True Knowledge Comes from Practice: Aligning LLMs with Embodied Environments via Reinforcement Learning 25 Jan 2024 weihaotan/twosome/twosome/virtualhome/inference_ppo_llm_v1.py 2e54f2ecd0d61798 ran licence not identified · pointer only
Unleashing Large-Scale Video Generative Pre-training for Visual Robot Manipulation 20 Dec 2023 bytedance/gr-1/evaluate_calvin.py 8e37b3c3fbc7c4a5 unverified Apache-2.0 (permissive)
Symmetry-Aware Robot Design with Structured Subgroups 31 May 2023 drdh/sard/design_opt/derl_terrain_builder.py 717d786b79662910 unverified MIT (permissive)
Outcome-directed Reinforcement Learning by Uncertainty & Temporal Distance-Aware Curriculum Goal Generation 27 Jan 2023 Stilwell-Git/Hindsight-Goal-Generation/learner/hgg.py d9e1da7c33277df3 unverified MIT (permissive)
MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations 12 Dec 2022 facebookresearch/modem/env.py 88d563b866d0550c unverified MIT recorded; this copy not marked cleared · pointer only
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning? 6 Dec 2022 susanbao/rmarl_code/rmarl/experiments/train_with_perturbed_network.py 24a950999549b85d unverified MIT recorded; this copy not marked cleared · pointer only
What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning? 6 Dec 2022 susanbao/rmarl_code/multiagent-particle-envs/make_env.py a87ea3074224c0e8 unverified MIT recorded; this copy not marked cleared · pointer only
Exploring through Random Curiosity with General Value Functions 18 Nov 2022 aditya-ramesh-10/exploring-through-rcgvf/mgrid_utils/env.py 2f470de73c0eda3c unverified MIT (permissive)
Self-Adaptive Driving in Nonstationary Environments through Conjectural Online Lookahead Adaptation 2022-10 (from id) panshark/cola/environment/atari.py d4af84ca3df6b55e unverified MIT (permissive)
Self-Adaptive Driving in Nonstationary Environments through Conjectural Online Lookahead Adaptation 2022-10 (from id) panshark/cola/environment/utils.py 39db26d3d8bcbd5e unverified MIT (permissive)
A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games 18 Jul 2022 quantumiracle/mars/mars/env/import_env.py 7bd11d0025602c10 unverified Apache-2.0 (permissive)
Fleet-DAgger: Interactive Robot Fleet Learning with Scalable Human Supervision 29 Jun 2022 berkeleyautomation/ifl_benchmark/env/make_utils.py 18c7c52ef841c0d4 unverified MIT (permissive)
EnvPool: A Highly Parallel Reinforcement Learning Environment Execution Engine 21 Jun 2022 vwxyzjn/envpool-cleanrl/ppo_continuous_action.py 3ee033959764bfd6 unverified MIT (permissive)
PAnDR: Fast Adaptation to New Environments from Offline Experiences via Decoupling Policy and Environment Representations 6 Apr 2022 tristandeleu/pytorch-maml-rl/maml_rl/samplers/sampler.py c2e1b382dce1e26b unverified MIT (permissive)
Context Meta-Reinforcement Learning via Neuromodulation 30 Oct 2021 dlpbc/nm-metarl/nm_cavia/rl/sampler.py 117528af3ffaa8d8 unverified MIT (permissive)
Cross Domain Robot Imitation with Invariant Representation 13 Sep 2021 zhaohengyin/irgail_example/imitation_learning/env.py e10629e58933d321 unverified MIT (permissive)
Character Controllers Using Motion VAEs 26 Mar 2021 electronicarts/character-motion-vaes/common/envs_utils.py 3a9ecb11281ffe04 unverified BSD-3-Clause (permissive)
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving 19 Oct 2020 mcederle99/MAD4QN-PS/util_rgb.py 4e65a552c24a864d unverified MIT recorded; this copy not marked cleared · pointer only
Deep Reinforcement Learning with Population-Coded Spiking Neural Network for Continuous Control 19 Oct 2020 combra-lab/pop-spiking-deep-rl/popsan_drl/popsan_ppo/ppo_cuda_norm.py c2985d7efcd097a6 unverified MIT (permissive)
Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning 2 Oct 2020 lmzintgraf/hyperx/vae.py 444582a5af765854 unverified licence not identified · pointer only
An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay 12 Jul 2020 sfujim/LAP-PAL/discrete/utils.py 4410a954fa46ebfb unverified MIT (permissive)
The NetHack Learning Environment 24 Jun 2020 Pieter-Cawood/Reinforcement-Learning/NLE_DQN/Agent.py 2f6ab6f543c54d76 unverified MIT (permissive)
DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction 16 Mar 2020 toshikwa/discor.pytorch/discor/env.py 6b8bc5549d0da3fa unverified MIT (permissive)
Fully Parameterized Quantile Function for Distributional Reinforcement Learning 5 Nov 2019 BY571/FQF-and-Extensions/wrapper.py 06fc02646fefa30a unverified MIT (permissive)
ZPD Teaching Strategies for Deep Reinforcement Learning from Demonstrations 26 Oct 2019 RevanMacQueen/LearningFromHumans/lfh/envs/atari.py 06128276029b84db unverified MIT (permissive)
Assistive Gym: A Physics Simulation Framework for Assistive Robotics 10 Oct 2019 Healthcare-Robotics/assistive-gym/assistive_gym/learn.py 4871fcb1309a2d93 unverified MIT (permissive)
Learning Transferable Cooperative Behavior in Multi-Agent Teams 4 Jun 2019 sumitsk/matrl/mape/make_env.py e90be820b06b21f1 unverified MIT (permissive)
Adversarial Policies: Attacking Deep Reinforcement Learning 25 May 2019 HumanCompatibleAI/adversarial-policies/experiments/planning/common.py 821d38fa42efe55c unverified MIT (permissive)
QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning 14 May 2019 Sonkyunghwan/QTRAN/Others/make_env.py ec3f64501ca07cda unverified no licence file found · pointer only
Actor-Attention-Critic for Multi-Agent Reinforcement Learning 5 Oct 2018 shariqiqbal2810/MAAC/utils/make_env.py 0fb08f714e49a890 unverified MIT (permissive)
Implicit Quantile Networks for Distributional Reinforcement Learning 14 Jun 2018 BY571/IQN/wrapper.py 06fc02646fefa30a unverified MIT (permissive)
World Models 27 Mar 2018 hsgrandhi/AIProject/env.py ba7be13bc51628c9 unverified MIT (permissive)
Distributed Prioritized Experience Replay 2 Mar 2018 haje01/distper/wrappers.py 94cca620996f3fbd unverified MIT (permissive)
Mean Field Multi-Agent Reinforcement Learning 15 Feb 2018 baoqianwang/iros22_darl1n/maddpg_o/experiments/train_normal.py e6e29c2c0e881c3c ran · fixture could not drive it no licence file found · pointer only
Mean Field Multi-Agent Reinforcement Learning 15 Feb 2018 baoqianwang/iros22_darl1n/maddpg_o/experiments/train_darl1n.py b6a7ee165ede9526 unverified no licence file found · pointer only
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks 9 Mar 2017 MoritzTaylor/maml-rl-tf2/maml_rl/sampler.py 4246e3602f23a27a unverified MIT (permissive)
The Option-Critic Architecture 16 Sep 2016 AshrithSagar/option-critic/oca/envs/utils.py e9c61c409e6c07d6 unverified MIT (permissive)
arXiv:ijcai2022_0528 GyChou/mcppoElegantRLforCarla/ray_elegantrl/interaction.py b917f33e17c81491 unverified MIT (permissive)
arXiv:aaai_29188 Jackory/RPBT/ppo/ppo_data_collectors.py 7d3b32023c111d73 unverified MIT (permissive)
arXiv:aaai_29188 Jackory/RPBT/toyexample/rppo.py c76cb158df6b31b2 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".

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