Saravanan666/pypi-org huggingface dataset:
Package: gym-saturation
gym-saturationgym-saturationis a collection ofGymnasiumenvironments for reinforcement
learning (RL) agents guiding saturation-style automated theorem
provers (ATPs) based on thegiven clause algorithm.There are two environments ingym-saturationfollowing the same
API:SaturationEnv:VampireEnv— forVampireprover, andIProverEnv— foriProver.gym-saturationcan be interesting for RL practitioners willing to
apply their experience to theorem proving without coding all the
logic-related stuff themselves.In particular, ATPs serving asgym-saturationbackends
incapsulate parsing the input formal language (usually, one of theTPTP(Thousands of Problems for Theorem
Provers) library), transforming the input formulae to theclausal
normal form, and logic
inference using rules such asresolutionandsuperposition.How to InstallAttention!If you want to useVampireEnvyou should have a
Vampire binary on your machine. For example, download the
latestrelease.To useIProverEnv, please download a stable iProverreleaseor build it fromthis commit.The best way to install this package is to usepip:pipinstallgym-saturationAnother option is to useconda:condainstall-cconda-forgegym-saturationOne can also run it in a Docker container (pre-packed withvampireandiproveroptbinaries):dockerbuild-tgym-saturationhttps://github.com/inpefess/gym-saturation.gitdockerrun-it--rm-p8888:8888gym-saturationjupyter-lab--ip=0.0.0.0--port=8888How to useOne can usegym-saturationenvironments as any other Gymnasium environment:importgym_saturationimportgymnasiumenv=gymnasium.make("Vampire-v0")# or "iProver-v0"# skip this line to use the default problemenv.set_task("a-TPTP-problem-filename")observation,info=env.reset()terminated,truncated=False,Falsewhilenot(terminatedortruncated):# apply policy (a random action here)action=env.action_space.sample()observation,reward,terminated,truncated,info=env.step(action)env.close()Or have a look at the basictutorial.For a bit more comprehensive experiments, please seethis project.More DocumentationMore documentation can be foundhere.Related Projectsgym-saturationis compatible with RL-frameworks such asRay
RLliband can leverage code embeddings such asCodeBERT.Other projects using RL-guidance for ATPs include:TRAILFLoP(seethe paperfor more details)lazyCoP(seethe paperfor more details)Other projects not using RL per se, but iterating a supervised
learning procedure instead:ENIGMA (several repos, e.g.this onefor
iProver; seethe paperfor
others)DeepireHow to ContributePlease followthe contribution guidewhile adhering tothe code of conduct.How to CiteIf you are writing a research paper and want to citegym-saturation, please use the followingDOI.
Package: gym-sch
基于simpy的仿真调度环境功能介绍本系统将露天矿山环境中的物料运输网络建模为双向二部图网络，通过高效的车辆分配，来实现吞吐量、工作时间等各种关键性能指标的优化。如下图所示，露天采矿运输系统包括两种主要设备类型：矿车和铲车。调度问题需要将车辆$r_i$分配到装载点并由铲车$s_j$进行装载。然后矿车将物料（即矿石或废物）运输到卸载点$d_k$。在完成装载或卸载后，车辆向调度员请求调度指令，调度员分别为车辆指定一个卸载点或一把铲子作为下一个目的地。车辆行程$<r_i，s_j，d_k>$为卸载点和铲子之间的一个循环。仿真环境可模拟车辆空载行驶、重载行驶、排队等待、装载及卸载等活动，并提供作业期间的累计产量信息。
系统奖励信息为当前动作指派的车辆完成一个完整的装卸载循环时间 $w$ 的负指数函数。直觉上，较短的行程时间使车辆能够在同一时间段内执行更多的行程，因此奖励值应该越大。如何使用安装pipinstallgym_sch简单用例importgymimportgym_schimportjsonimportnumpyasnpprint(f'gym version:{gym.__version__}')# 系统参数para_config={"seed":20,"shovels":8,"dumps":6,"rout_distance":[[2.41,6.51,10.86,11.16,7.94,11.58,14.7,6.7],[10.82,8.07,5.05,1.66,11.51,2.87,11.4,11.11],[6.28,2.79,10.48,9.59,10.41,11.42,3.96,5.17],[9.06,9.4,10.1,13.93,5.97,10.25,5.33,5.46],[13.23,12.14,5.95,10.7,7.21,3.89,14.65,7.87],[14.63,4.01,10.77,6.11,13.76,6.08,12.73,12.9]],"horizon":480,"payload":220,"truck_num":60,"load_capacity":[2000,2000,2000,2000,2000,2000,2000,2000],"unload_capacity":[2375,2375,2375,2375,2375,2375],"shovel_idle_power":[6.6,9.5,9.5,6.6,6.6,9.5,9.5,6.6],"shovel_work_power":[117,130,130,117,117,130,130,117],"dump_target_mass":[15000,15000,15000,15000,15000,15000],"shovel_target_mass":[15000,15000,15000,15000,15000,15000,15000,15000]}# 设置随机种子np.random.seed(para_config["seed"])# 获取仿真环境并初始化env=gym.make('sch-v6')env.para_set(para_config)env.reset(False)done_n=Falseepisode=0# 默认运输时间为仿真时间 480minwhilenotdone_n:episode=episode+1# 动作为 [0, shovels+dumps) 区间内的离散值, 其中shovels为挖机数量，dumps为卸点数量action=env.action_space.sample()print(f'Choose Action{action}')'''依次返回生产状态信息(array(float)),奖励信息(float),班次是否结束(bool),过程信息(dict),累计产量(array(float)), ...'''obs_n,reward_n,done_n,info,mass,_,_=env.step(action)env.close()详细参数说明horizon: 系统运行时间，到此时间系统自动中断；shovels: 装载（挖掘）设备数量；dumps: 卸载设备数量；truck_num: 矿车数量；rout_distance:shovels$\times$dumps的路网矩阵（千米）；payload: 车辆有效载荷（吨）；load_capacity: 装载设备装载能力（吨\时）；unload_capacity: 卸载设备卸载能力（吨\时）；dump_target_mass: 卸载点目标产量（吨）；shovel_target_mass: 装载点目标产量（吨）；shovel_idle_power: 装载设备空闲油耗；shovel_work_power: 装载设备工作油耗；对于利用这项工作的研究人员，请引用以下文章：X. Zhang, A. Guo, Y. Ai, B. Tian and L. Chen, "Real-Time Scheduling of Autonomous Mining Trucks via Flow Allocation-Accelerated Tabu Search," in IEEE Transactions on Intelligent Vehicles, vol. 7, no. 3, pp. 466-479, Sept. 2022, doi: 10.1109/TIV.2022.3166564.X. Zhang, L. Chen, Y. Ai, B. Tian, D. Cao and L. Li, "Scheduling of Autonomous Mining Trucks: Allocation Model Based Tabu Search Algorithm Development," 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA, 2021, pp. 982-989, doi: 10.1109/ITSC48978.2021.9564491.
Package: gym-shopping-cart
gym-display-advertisingAn OpenAI Gym for Shopping Cart Reinforcement Learning.This is a project byWinder Research, a Cloud-Native Data Science consultancy.Installationpip install gym-shopping-cartUsageThis example will use the small example data included in the repo.importgymimportgym_shopping_cartenv=gym.make("ShoppingCart-v0")episode_over=Falserewards=0whilenotepisode_over:state,reward,episode_over,_=env.step(env.action_space.sample())print(state,reward)rewards+=rewardprint("Total reward:{}".format(rewards))Real Shopping Cart DataThis environment uses real shopping cart information from theInstacart dataset.To help read this data the library also comes with a data parser. This loads the raw data and cleans the data to be in a format expected by the environment.CreditsGitlab icon made byFreepikfromwww.flaticon.com.
Package: gym-simple-cliffworld
gym-simple-cliffworldA simplified version of Cliffworld in an OpenAI Gym Environment.This is a project byWinder Research, a Cloud-Native Data Science consultancy.Installationpip install gym-simple-cliffworldUsageimportgymimportgym_simple_cliffworldenv=gym.make("SimpleCliffworld-v0")episode_over=Falserewards=0whilenotepisode_over:state,reward,episode_over,_=env.step(env.action_space.sample())print(state,reward)rewards+=rewardprint("Total reward:{}".format(rewards))CreditsGitlab icon made byPayungkeadfromwww.flaticon.com.
Package: gym-simplifiedtetris-AVELA
Gym-SimplifiedTetrisReport Bug·Request Feature·Suggestions🟥 Simplified Tetris environments compliant with OpenAI Gym's APIGym-SimplifiedTetris is a pip installable package that creates simplified Tetris environments compliant withOpenAI Gym's API. Gym's API is the field standard for developing and comparing reinforcement learning algorithms.There are currentlythree agentsand64 environmentsprovided. The environments are simplified because the player must select the column and piece's rotation before the piece starts falling vertically downwards. If one looks at the previous approaches to the game of Tetris, most of them use this simplified setting.1. Installation2. Usage3. Future work4. Acknowledgements1. InstallationThe package is pip installable:pipinstallgym-simplifiedtetrisOr, you can copy the repository by forking it and then downloading it using:gitclonehttps://github.com/<YOUR-USERNAME>/gym-simplifiedtetrisPackages can be installed using pip:cdgym-simplifiedtetris
pipinstall-rrequirements.txt2. UsageThe fileexamples/envs.pyshows two examples of using an instance of thesimplifiedtetris-binary-20x10-4-v0environment for ten games. You can create an environment usinggym.make, supplying the environment's ID as an argument.importgymimportgym_simplifiedtetrisenv=gym.make("simplifiedtetris-binary-20x10-4-v0")obs=env.reset()# Run 10 games of Tetris, selecting actions uniformly at random.episode_num=0whileepisode_num<10:env.render()action=env.action_space.sample()obs,reward,done,info=env.step(action)ifdone:print(f"Episode{episode_num+1}has terminated.")episode_num+=1obs=env.reset()env.close()Alternatively, you can import the environment directly:fromgym_simplifiedtetrisimportSimplifiedTetrisBinaryEnvasTetrisenv=Tetris(grid_dims=(20,10),piece_size=4)3. Future workNormalise the observation spaces.Implement an action space that only permits the agent to take non-terminal actions.Implement more shaping rewards: potential-style, potential-based, dynamic potential-based, and non-potential. Optimise their weights using an optimisation algorithm.Write end-to-end and integration tests using pytest.Perform mutation and property-based testing using mutmut and Hypothesis.Use Coverage.py to increase code coverage.4. AcknowledgementsThis package utilises several methods from thecodebasedeveloped by andreanlay (2020) and thecodebasedeveloped by Benjscho (2021).
Package: gym-snake-game
Snake game for OpenAI GymSnake game for OpenAI gymQuick Startimportgym_snake_gameimportgym# both workenv=gym.make('Snake-v0',render_mode='human')env=gym_snake_game.make('Snake-v0',render_mode='human')env.reset()# for human playingenv.play()# for ai playingwhileTrue:obs,reward,done,truncated,info=env.step(env.action_space.sample())ifdone:breakenv.close()Ming Yu
Package: gym-socks
SOCKS :socks:NOTE: SOCKS is still under active (alpha) development 🚧👷🚧. Code is provided
as-is, and some algorithms may not work as intended before the initial release. Please
check back soon for the official release.SOCKS is a suite of algorithms for stochastic optimal control using kernel methods.It runs on top ofOpenAI Gym, and comes with several classic
controls Gym environments. In addition, it can integrate with many pre-existing Gym
environments.InstallationTo install the toolbox, usepip install gym-socks. Alternatively, download the code
from the GitHub repo and install usingpip install .from the code directory.We support Python versions 3.7, 3.8, and 3.9 on Linux and macOS. We do not officially
support Windows.ExamplesSOCKS comes with several examples in the GitHub repo. In order to run the examples,
first install the package and usepython examples/<example>from the code directory.For example,python python examples/control/tracking.pywill run the optimal control
algorithm on the tracking benchmark using nonholonomic vehicle dynamics.CitationIn order to cite the toolbox, use the following bibtex entry:@inproceedings{thorpe2022hscc,
  title={{SOCKS}: A Kernel-Based Stochastic Optimal Control and Reachability Toolbox},
  authors={Thorpe, Adam J. and Oishi, Meeko M. K.},
  year={2022},
  booktitle={Proceedings of the 25th ACM International Conference on Hybrid Systems: Computation and Control (to appear)},
}
Package: gym-softrobot
Soft-Robot Control Environment (gym-softrobot)The environment is designed to leverage wide-range of reinforcement learning methods into soft-robotics control.
Our inspiration is from slender-body living creatures, such as octopus or snake.
The code is based onPyElastica, an open-source physics simulation for highly-deformable slender structure.
We intend this package to be easy-to-install and fully compatible toOpenAI Gymand other available RL algorithms.The package is under development, in Alpha phase. Detail roadmap for Q2-2022 will be available.Installationpipinstallgym-softrobotTo test the installation, we provide few debugging scripts.
The environment can be tested using the following command.python-mgym_softrobot.debug.make# Make environment and run 10 stepspython-mgym_softrobot.debug.registry# Print gym-softrobot environmentRequirements:Python 3.8+OpenAI Gym 0.21.0PyElastica 0.2+Matplotlib (optional for display rendering and plotting)POVray (optional for 3D rendering)RenderingWe support two different backends for the rendering:POVrayandMatplotlib.
The default is set to use POVray, but the configuration can be switched by adding following lines.fromgym_softrobot.configimportRendererTypegym_softrobot.RENDERER_CONFIG=RendererType.MATPLOTLIB# Default: POVRAYPOVrayTo make a good-looking 3D videos and figures, we usePOVraypython wrapperVapory.
POVray is not a requirement to run the environment, but it is necessary to useenv.render()function as typical gym environment.If you would like to testPOVraywithgym-softrobot, usepython-mgym_softrobot.debug.render# Render 10 frames using vaporyMatplotlibWe provide secondary rendering tool usingMatplotlibfor a quick debugging and sanity checking.Reinforcement Learning ExampleWe tested the environment usingStable Baselines3for centralized control.
More advanced algorithms are still under development.If you have your own algorithm that you would like to test with our environment, you are welcome to reach out to us.CitationPlease use this bibtex to cite in your publications:@misc{gym_softrobot,
  author = {Chia-Hsien Shih, Seung Hyun Kim, Mattia Gazzola},
  title = {Soft Robotics Environment for OpenAI Gym},
  year = {2022},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/skim0119/gym-softrobot}},
}Environment DocumentationThe description of each environment is available indocumentation.ContributionWe are currently developing the package internally.
We plan to deploy the package to open-development in Q2-2022.Author
Package: gym-stopping
gym-stoppingBrief description:control space: discrete {0,1}, continue or stopstate space: continuous (0, \infty), [0,1], stock price and time to maturitydefault parameters: spot = 90.0, strike = 100.0, drift = 0.02, sigma = 0.20, maturity = 1.0, periods = 365To install:pip install gym-stoppingimport gym_stoppingTo load:env = gym.make("stopping-v0")To update version after changes:change version to, e.g., 1.0.7 from setup.py filegit clonehttps://github.com/claudia-viaro/gym-stopping.gitcd gym-updatepython setup.py sdist bdist_wheeltwine check dist/*twine upload --repository-urlhttps://upload.pypi.org/legacy/dist/*
Package: gym-tetris
gym-tetrisAnOpenAI Gymenvironment for Tetris on The
Nintendo Entertainment System (NES) based on thenes-pyemulator.InstallationThe preferred installation ofgym-tetrisis frompip:pipinstallgym-tetrisUsagePythonYou must importgym_tetrisbefore trying to make an environment.
This is because gym environments are registered at runtime. By default,gym_tetrisenvironments use the full NES action space of 256
discrete actions. To constrain this,gym_tetris.actionsprovides
an action list calledMOVEMENT(20 discrete actions) for thenes_py.wrappers.JoypadSpacewrapper. There is alsoSIMPLE_MOVEMENTwith a reduced action space (6 actions). For exact details,
seegym_tetris/actions.py.fromnes_py.wrappersimportJoypadSpaceimportgym_tetrisfromgym_tetris.actionsimportMOVEMENTenv=gym_tetris.make('TetrisA-v0')env=JoypadSpace(env,MOVEMENT)done=Trueforstepinrange(5000):ifdone:state=env.reset()state,reward,done,info=env.step(env.action_space.sample())env.render()env.close()NOTE:gym_tetris.makeis just an alias togym.makefor
convenience.NOTE:remove calls torenderin training code for a nontrivial
speedup.Command Linegym_tetrisfeatures a command line interface for playing
environments using either the keyboard, or uniform random movement.gym_tetris-e<environmentID>-m<`human`or`random`>EnvironmentsThere are two game modes define in NES Tetris, namely, A-type and B-type.
A-type is the standard endurance Tetris game and B-type is an arcade style mode
where the agent must clear a certain number of lines to win. There are three
potential reward streams: (1) the change in score, (2) the change in number of
lines cleared, and (3) a penalty for an increase in board height. The table
below defines the available environments in terms of the game mode (i.e.,
A-type or B-type) and the rewards applied.EnvironmentGame Modereward scorereward linespenalize heightTetrisA-v0A-type✅✕✕TetrisA-v1A-type✕✅✕TetrisA-v2A-type✅✕✅TetrisA-v3A-type✕✅✅TetrisB-v0B-type✅✕✕TetrisB-v1B-type✕✅✕TetrisB-v2B-type✅✕✅TetrisB-v3B-type✕✅✅infodictionaryTheinfodictionary returned by thestepmethod contains the following
keys:KeyTypeDescriptioncurrent_piecestrthe current piece as a stringnumber_of_linesintthe number of cleared lines in [0, 999]scoreintthe current score of the game in [0, 999999]next_piecestrthe next piece on deck as a stringstatisticsdictthe number of tetriminos dispatched (by type)board_heightintthe height of the board in [0, 20]CitationPlease citegym-tetrisif you use it in your research.@misc{gym-tetris,
  author ={Christian Kauten},
  howpublished ={GitHub},
  title ={{Tetris (NES)}for{OpenAI Gym}},
  URL ={https://github.com/Kautenja/gym-tetris},
  year ={2019},}ReferencesThe following references contributed to the construction of this project.Tetris (NES): RAM Map.Data Crystal ROM Hacking.Tetris: Memory Addresses.NES Hacker.Applying Artificial Intelligence to Nintendo Tetris.MeatFighter.
Package: gym-tictactoe
Tic Tac Toe Game in OpenAI GymThe 3D version of Tic Tac Toe is implemented as an OpenAI's Gym environment. Thelearningfolder includes several Jupyter notebooks for deep neural network models used to implement a computer-based player.ComplexityThe traditional (2D) Tic Tac Toe has a very small game space (9^3). In comparison, the 3D version in this repo has a much larger space which is in the order of 81^3. This makes computer-based players using search and pruning techniques of the game space prohibitively expensive.Rather, the current learning models are based on policy gradient and deep Q-learning. TheDQN modelhas produced very promising results. Feel free to experience on your own and contribute if interested. ThePG-based modelneeds more work :)ContributionsThe repo is also open for pull requests and collaborations both in game development as well as learning.DependenciesBase dependency:gym.Plot-rendering dependencies:numpy,matplotlib.DQN learning dependencies:tensorflow,numpy.InstallationTo install run:#Inyourvirtualenvironmentpip install gym-tictactoeUsageCurrently 2 types of environments with different rendering modes are supported.Textual renderingTo use textual rendering create environment astictactoe-v0like so:importgymimportgym_tictactoedefplay_game(actions,step_fn=input):env=gym.make('tictactoe-v0')env.reset()# Play actions in action profileforactioninactions:print(env.step(action))env.render()ifstep_fn:step_fn()returnenvactions=['1021','2111','1221','2222','1121']_=play_game(actions,None)The output produced is:Step 1:
- - -    - - -    - - -    
- - x    - - -    - - -    
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Step 2:
- - -    - - -    - - -    
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- - -    - - -    - - -    
- - x    - o -    - - x    
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- - -    - - -    - - -    
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Step 5:
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- - X    - o X    - - X    
- - -    - - -    - - oThe winning sequence after gameplay:(0,2,1), (1,2,1), (2,2,1).Plotted renderingTo use textual rendering create environment astictactoe-plt-v0like so:importgymimportgym_tictactoedefplay_game(actions,step_fn=input):env=gym.make('tictactoe-plt-v0')env.reset()# Play actions in action profileforactioninactions:print(env.step(action))env.render()ifstep_fn:step_fn()returnenvactions=['1021','2111','1221','2222','1121']_=play_game(actions,None)This produces the following gameplay:Step 1:Step 2:Step 3:Step 4:Step 5:DQN LearningThe current models are underlearningfolder. SeeJupyter notebookfor a DQN learning with a 2-layer neural network and using actor-critic technique.Sample game plays produced by the trained model (the winning sequence is(0,0,0), (1,0,0), (2,0,0)):
Package: gym-tool-use
Gym Tool Usegymtool use environments.$pipinstallgym-tool-useUsageimportgym_tool_use# import to register gym envsenv=gym.make("TrapTube-v0")observation=env.reset()action=env.action_space.sample()observation_next,reward,done,info=env.step(action)image=env.render(mode="rgb_array")# also supports mode="human"EnvironmentsThe following environments are registered:"TrapTube-v0"(base task)"PerceptualTrapTube-v0""StructuralTrapTube-v0""SymbolicTrapTube-v0""PerceptualSymbolicTrapTube-v0""StructuralSymbolicTrapTube-v0""PerceptualStructuralTrapTube-v0""PerceptualStructuralSymbolicTrapTube-v0"BaselinesBaseline implementations here:https://github.com/fomorians/tool-useDevelopmentDevelopment is started withpipenv.$pipenvinstall
$pipenvshell
Package: gym-toytext
gym_toytextThis repository contains the text environments previously present in OpenAI Gym <0.20. These environments had been in the master branch ofopenai/gymbut later excluded inthis pull.List of environmentsenvironmentcommit historyfirst committerGuessingGame-v0guessing_game.py@JKCooper2HotterColder-v0hotter_colder.py@JKCooper2KellyCoinflip-v0andKellyCoinflipGeneralized-v0kellycoinflip.py@gwernNChain-v0nchain.py@machinautRoulette-v0roulette.py@gdbCompatibilitygym>=0.26: Please usegym_toytext>=0.26.gym>=0.19, gym<0.26: Please usegym_toytext==0.25.Installpip install gym-toytextUsageimportgymimportgym_toytextenv=gym.make("GuessingGame-v0")observation,info=env.reset()low,high=env.action_space.low,env.action_space.highwhileTrue:action=(low+high)/2.observation,reward,termination,truncation,info=env.step(action)ifterminationortruncation:breakifobservation==1:low=actionelifobservation==3:high=actionenv.close()
Package: gym-trading-env
Gym Trading Env is an Gymnasium environment for simulating stocks and training Reinforcement Learning (RL) trading agents.
It was designed to be fast and customizable for easy RL trading algorithms implementation.|Documentation|Key featuresThis package aims to greatly simplify the research phase by offering :Easy and quick download technical data on several exchangesA simple and fast environment for the user and the AI, but which allows complex operations (Short, Margin trading).A high performance rendering (can display several hundred thousand candles simultaneously), customizable to visualize the actions of its agent and its results.(Coming soon) An easy way to backtest any RL-Agents or any kindInstallationGym Trading Env supports Python 3.9+ on Windows, Mac, and Linux. You can install it using pip:pipinstallgym-trading-envOr using git :gitclonehttps://github.com/ClementPerroud/Gym-Trading-EnvDocumentation available here
Package: gym-twolinkarm-env
gym-twolinkarm-envInstallationpip install gym-twolinkarm-envUsage example# coding: utf-8importgymimportgym_twolinkarm_envenv=gym.make("TwoLinkArmEnv-v0")forepinrange(10):env.reset()forstepinrange(200):action=env.action_space.sample()obs,rew,done,info=env.step(action)env.render()detailenvstatephisicsrewardaction
Package: gym-update1
gym-update1To installgit clonehttps://github.com/claudia-viaro/gym-update1.gitcd gym-update1!pip install gym-update1import gymimport gym_updateenv =gym.make('update-v0')To change versionchange version to, e.g., 1.0.7 from setup.py filegit clonehttps://github.com/claudia-viaro/gym-update1.gitcd gym-update1python setup.py sdist bdist_wheeltwine check dist/*twine upload --repository-urlhttps://upload.pypi.org/legacy/dist/*
Package: gym-update2
gym-update2Same as gym-update BUT state space = sizeTo installgit clonehttps://github.com/claudia-viaro/gym-update2.gitcd gym-update1!pip install gym-update2import gymimport gym_updateenv =gym.make('update-v0')To change versionchange version to, e.g., 1.0.7 from setup.py filegit clonehttps://github.com/claudia-viaro/gym-update2.gitcd gym-update2python setup.py sdist bdist_wheeltwine check dist/*twine upload --repository-urlhttps://upload.pypi.org/legacy/dist/*