Saravanan666/pypi-org huggingface dataset:
Package: gym-demo
gym-demoExplore OpenAI Gym environmentsThis package provides thegym-democommand, which allows you to
explore the variousOpen AI gymenvironments installed on your
system.This allows you to get a quick overview of an environment before you
start working on it. You will get information about the environmentsobservation space,action spaceas well as therewardsyou can
expect to get and other available information.InstallationYou can install OpenAI Gym andgym-demousingpip:$ pip install gym[atari]
$ pip install gym-demoUsageUsegym-demo --helpto display usage information and a list of
environments installed in your Gym.$ gym-demo --helpStart a demo of an environment to get information about its observation
and action space and observe the rewards an agent gets during a random
run.$ gym-demo SpaceInvaders-ram-v4
Environment: SpaceInvaders-ram-v4

Observation Space: Box(128,)
Low values:
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
High values:
[255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255 255
 255 255]

Action Space: Discrete(6)
Action meanings: ['NOOP', 'FIRE', 'RIGHT', 'LEFT', 'RIGHTFIRE', 'LEFTFIRE']


Running environment demonstration...
Unique environment information is output to standard out:
Reward: 0.0, Done: False, Info: {'ale.lives': 3}
Reward: 5.0, Done: False, Info: {'ale.lives': 3}
Reward: 0.0, Done: False, Info: {'ale.lives': 3}
Reward: 10.0, Done: False, Info: {'ale.lives': 3}
Reward: 0.0, Done: False, Info: {'ale.lives': 3}
Reward: 15.0, Done: False, Info: {'ale.lives': 3}
Reward: 0.0, Done: False, Info: {'ale.lives': 3}
Reward: 20.0, Done: False, Info: {'ale.lives': 3}
Reward: 0.0, Done: False, Info: {'ale.lives': 3}
Reward: 25.0, Done: False, Info: {'ale.lives': 3}
Reward: 0.0, Done: False, Info: {'ale.lives': 3}
Reward: 0.0, Done: False, Info: {'ale.lives': 2}
Reward: 30.0, Done: False, Info: {'ale.lives': 2}
Reward: 0.0, Done: False, Info: {'ale.lives': 2}
Reward: 0.0, Done: False, Info: {'ale.lives': 1}
Reward: 5.0, Done: False, Info: {'ale.lives': 1}
Reward: 0.0, Done: False, Info: {'ale.lives': 1}
Reward: 10.0, Done: False, Info: {'ale.lives': 1}
Reward: 0.0, Done: False, Info: {'ale.lives': 1}
Reward: 15.0, Done: False, Info: {'ale.lives': 1}
Reward: 0.0, Done: False, Info: {'ale.lives': 1}
Reward: 20.0, Done: False, Info: {'ale.lives': 1}
Reward: 0.0, Done: False, Info: {'ale.lives': 1}
Reward: 0.0, Done: True, Info: {'ale.lives': 0}Get mode information aboutOpen AI gymon theirdocumentation website.
Package: gym-dino
OpenAI Gym Environment of the Chrome T-Rex GameA pygame based port of the Chrome T-Rex Game as an OpenAI Gym Environment.Important infoYou can change the FPS of the game by adjusting the env.FPS value. By default, it is at 60.Action Space = [0, 1, 2]0 : No action1 : Duck2 : JumpYou can install this from PYPI:pip3 install gym-dinoYou can import it as:import gym_dinoSmall backstoryI am currently working on some reinforcement learning research problems and really wanted a simple yet effective environment to train an agent on. I realised that the Chrome game ain't bad at all especially in this scenario.So have fun, any help to further develop on this is welcome.Right now, onlyrender,step,resetandclosefunction have been implemented.Special thanks toShivam Shekar'simplementation of the original game using pygame.
Package: gym-discrete
Gym-style APIThe domanin features a continuos state and a dicrete action space.The environment initializes:cross-sectional dataset with variables X_a, X_s, Y and N observations;logit model fitted on the dataset, retrieving parameters \theta_0, \theta_1, \theta_2;The agent:sees all patients;predict risk of admission \rho, using initialized parameterssample an action (50 possible values between -2 and 2)if risk > 0.2:replace Xa by g, where g(\rho, Xa) is obtained using the patient's risk and Xa valueelse:do not intervene, X_a stays the samegive reward equal to average risk of admission, using predicted Y, initial parameters and sampled valuesTo installgit clonehttps://github.com/claudia-viaro/gym-discrete.gitcd gym-discrete!pip install gym-discreteimport gym_discreteenv =gym.make('discrete-v0')To change versionchange version to, e.g., 1.0.7 from setup.py filegit clonehttps://github.com/claudia-viaro/gym-discrete.gitcd gym-discretepython setup.py sdist bdist_wheeltwine check dist/*twine upload --repository-urlhttps://upload.pypi.org/legacy/dist/*
Package: gym-display-advertising
gym-display-advertisingAn OpenAI Gym for Display Advertisment Reinforcement LearningInstallationpip install gym-display-advertisingUsageimportgym_display_advertisingenv=gym.make("StaticDisplayAdvertising-v0")episode_over=Falsewhilenotepisode_over:state,reward,episode_over,_=env.step(env.action_space.sample())print(state,reward)importgym_display_advertisingenv=gym.make("DisplayAdvertising-v0")episode_over=Falsewhilenotepisode_over:state,reward,episode_over,_=env.step(env.action_space.sample())print(state,reward)Real Ad Bidding DataThe repository contains real-life bidding data from a single merchant and loads this by default. If you want to load more data follow the instructions in themake-ipinyou-data repositoryto create the data.Then use the helper classProcessedIPinYouDatato load the data and pass the dataframe into thegym.makecommand.importpathlibimportgymimportgym_display_advertisingipinyou=gym_display_advertising.data.ProcessedIPinYouData(directory=pathlib.Path("path/to/file"))training_data,_=ipinyou.get_merchant_data(2997)env=gym.make("DisplayAdvertising-v0",data=training_data)state,reward,_,_=env.step(env.action_space.sample())print(state,reward)AcknowledgementsThis is a project byWinder Research, a Cloud-Native Data Science consultancy.
Package: gym-distractions
Gym DistractionsThis package provides a number of distractions that can be added to pixel based gym environments.
It directly includes the creation of MuJoCo Environments with these distractions.InstallationRelease install from pypipipinstallgym-distractionsInstallation of latest from GIT Repositorypipinstall-egit+https://github.com/sebimarkgraf/gym-distractions.gitUsing MuJoCo EnvironmentsUse the MuJoCo environments by importing it and using the make() method.import gym_distractions

env = gym_distractions.make(domain_name,
                    task_name,
                    distract_type,
                    ground,
                    difficulty,
                    background_dataset_path,
                    train_or_val,
                    seed,
                    visualize_reward,
                    from_pixels,
                    height,
                    width,
                    camera_id,
                    frame_skip,
                    episode_length,
                    environment_kwargs,
                    time_limit,
                    channels_first
                    )The following is the explanation of the parameters which used to control the background/foreground,
and the rest of the parameters are the same as the original dmc2gym code.distract_type             : choice{'color', 'noise', 'dots', 'videos'}, default: None
ground                    : choice{'background', 'forground', 'both'}, default: None,
                            'both' only used for 'videos' distractor, 'color' and 'noise' only have 'background'
difficulty                : choice{'easy', 'medium', 'hard'}, default: 'hard'
                            only useful for 'dots' and 'videos' distractor
                            when use 'dots' distractor, set num_dots to: 'hard'=16, 'medium'=8, 'easy'=5;
                            when use 'videos' distract type, set num_video to: 'hard'=all, others the same as 'dots'
intensity                 : 0-1, default: 1
                            distracting intensity(non-transparency?): 1 is all distrated, 0 is same as original env
background_dataset_path   : where you put your video/image dataset, only useful for 'videos'
train_or_val              : choice{'train', 'val'}, default: None
                            when use DAVIS Dataset, can divided it to train-set and validation-setwhen you use 'dots', by default, distractors are repeated, and movements of dots obey the dynamics of
an ideal gas with no collison. If you want to change those default settings, or you want to modify
sizes/velocitys/positions/quantity/or some others of dots,
you can modify them in file 'background_source.py', class 'RandomDotsSource' by yourself.AttributionBased on work of:Philipp BeckerYiping WeiYitian YangCiting This WorkIf you find this work helpful, please cite the corresponding publicationTODO
Package: gym-dmc
Link to other OpenAI Gym Plugins:gym-sawyergym-toy-navUpdate Log2022-01-13: Add space_dtype for overriding the dtype for the
state and action spaces. Default to None, need to set tofloat/np.float32for pytorch_SAC implementation.2022-01-11: Added aenv._get_obs()method to allow one to
obtain the observation after resetting the environment.Version:
``v0.2.1``How To UseUsage pattern:importgymenv=gym.make("dmc:Pendulum-swingup-v1")For the full list of environments, you can print:fromdm_control.suiteimportALL_TASKSprint(*ALL_TASKS,sep="\n")# Out[2]: ('acrobot', 'swingup')#         ('acrobot', 'swingup_sparse')...We register all of these environments using the following pattern:acrobot task “swingup_sparse” becomesdmc:Acrobot-swingup_sparse-v1You can see the usage pattern inhttps://github.com/geyang/gym_dmc/blob/master/specs/test_gym_dmc.py:env=gym.make('dmc:Walker-walk-v1',frame_skip=4,space_dtype=np.float32)assertenv.action_space.dtypeisnp.float32assertenv.observation_space.dtypeisnp.float32env=gym.make('dmc:Walker-walk-v1',frame_skip=4)assertenv._max_episode_steps==250assertenv.reset().shape==(24,)env=gym.make('dmc:Walker-walk-v1',from_pixels=True,frame_skip=4)assertenv._max_episode_steps==250env=gym.make('dmc:Cartpole-balance-v1',from_pixels=True,frame_skip=8)assertenv._max_episode_steps==125assertenv.reset().shape==(3,84,84)env=gym.make('dmc:Cartpole-balance-v1',from_pixels=True,frame_skip=8,channels_first=False)assertenv._max_episode_steps==125assertenv.reset().shape==(84,84,3)env=gym.make('dmc:Cartpole-balance-v1',from_pixels=True,frame_skip=8,channels_first=False,gray_scale=True)assertenv._max_episode_steps==125assertenv.reset().shape==(84,84,1)Note, the ``max_episode_steps`` is calculated based on the
``frame_skip``.All DeepMind control domains terminate after 1000
simulation steps. So forframe_skip=4, themax_episode_stepsshould be 250.Built with :heart: by Ge Yang
Package: gym-dofbot
Gym-DofbotOpenAI Gym environment for low-cost YahboomDofbotGym-Dofbot is an reinforcement-learning friendly Gym environment that is powered by Pybullet. There are certain features that make this package somewhat useful:FeaturesRe-modelled camera module that supports real-time streaming of images in the frontAllows precise position control for the servo groupPotential for multi-agent learning and object interactionInstallationInstall the numpy, pybullet and gym.
Install via pip:pip install gym-dofbot==0.1.0Install via Github repository:cdgym_dofbot
pipinstall-e.DevelopmentWant to contribute? Great!Code is straightforward enough, feel free to fork and add more content to it.LicenseGPL
Package: gym-donkeycar
OpenAI Gym Environments for Donkey CarDonkey Car OpenAI GymFree software: MIT licenseDocumentation:https://gym-donkeycar.readthedocs.io/en/latest/InstallationInstall with pippipinstallgym-donkeycarDownload simulator binaries:https://github.com/tawnkramer/donkey_gym/releasesExample Usageimportgymenv=gym.make("donkey-generated-track-v0)Environments“donkey-warehouse-v0”“donkey-generated-roads-v0”“donkey-avc-sparkfun-v0”“donkey-generated-track-v0”CreditsOriginal Source CodeTawn Kramer -https://github.com/tawnkramer/donkey_gymRoma Sokolkov -https://github.com/r7vme/donkey_gymRelease EngineerThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History1.0.0 (2019-07-26)First release on PyPI.1.0.1 - 1.0.11 (2019-08-04)Testing out deploy systemUpdate credits/authorsflake8
Package: gym-embedding-world
OpenAI gym Embedding worldAn eight-dimensional hypercube graph.Two word embedding mapping compatible withOpenAI gym.Requirements:Python 3.5+OpenAI GymNumPyGensimInstall environment on anaconda$ conda env create -f gym-embedding-world/environment.yml
$ source embedding-world
$ pip install -e gym-embedding-world/.Install environment on colab!git clone "https://github.com/SamirMoustafa/gym-embedding-world.git"
!pip install -e gym-embedding-world/.
!mv gym-embedding-world gym-embedding-world-org
!cp -r gym-embedding-world-org/embedding_world /content
!ls embedding_worldUsage$ python >>> import gym
$ python >>> import embedding_world
$ python >>> env = gym.make('embedding_world-v0')
$ python >>> env.set_paths(embedding_from_file="... YOUR EMBEDDING PATH TO MAP FROM IT  ...",
                           embedding_to_file  ="... YOUR EMBEDDING PATH TO MAP TO IT  .....")
$ python >>> env.production_is_off()
$ python >>> env.set_sentences('... YOUR SENTENCE TO TRANSLATE FROM IT ...', 
                               '... YOUR SENTENCE TO TRANSLATE TO IT .....')
$ python >>> state, reward, done, info = env.step('dim(0)+1')embedding_world-v0Embedding world is a simple environment based on OpenAI gym, that loads two-word embedding e.g.Stanfrod GloVeorfacebook fastText modelswith N-dimension and moves from one word(s) embedding-location to the other embedding using an agent actions such that actions that could be taken are2N + 1actions{dimension(i)+1, dimension(i)-1}∪{pickup}∀iin range from 1 to Nwhich deterministically cause the corresponding state transitions
but actions that would take an agent of the grid leave a state unchanged.
The reward is negative for all transition until the goal is reached.
The terminal state(goal) is represented in a vector/s.This environment has been built as part of a graduation project atUniversity of Alexandria, Department of Computer SciencePlease use this bibtex if you want to cite this repository in your publications:@misc{embedding_world,
    author = {Samir Moustafa},
    title = {Embedding Environment for OpenAI Gym},
    year = {2019},
    publisher = {GitHub},
    journal = {GitHub repository},
    howpublished = {\url{https://github.com/SamirMoustafa/gym-embedding-world}}
}
Package: gym-fetch
Extended Taskset for the Fetch RobotInstallationyou can do:pipinstallgym-fetchAlternatively, you can clone this repo and install under development
mode:git clone <this repo>
cd <this repo>
pip install -e .EnvironmentsWe extend existing Fetch environments from gym, with 7 new manipulation
tasks. Thegym.Fetchenvironment are much better engineered than the
sawyer environments thatmetaworlduses. They are faster to
initialize, and have a small (50 step) maximum episode length, making
these environments faster to train on.We might or might not need to extend themax_episode_stepson
more complex tasks.Reach-v2Push-v2PickPlace-v2Slide-v2For Up-To-Date EnvironmentsAll documentations are maintained in thehttps://github.com/geyang/gym_fetch/blob/master/specsfolder,
where each task set is one markdown file.original gym fetch tasks:https://github.com/geyang/gym_fetch/blob/master/specs/gym_fetch.md``bin`` tasks:https://github.com/geyang/gym_fetch/blob/master/specs/bin.md``bin_debug`` tasks:https://github.com/geyang/gym_fetch/blob/master/specs/bin_debug.md``box`` tasks:https://github.com/geyang/gym_fetch/blob/master/specs/box.md``drawer`` tasks:https://github.com/geyang/gym_fetch/blob/master/specs/drawer.mdThe multi-task environments are still under development. They are
located underfetch├──tasksets├──box_block.md├──box_block.pyPrimitive Single Task EnvironmentsThe tasks involve a single primitive action such as open/closing a box,
or a drawer. They do not additionally involve placing an object into the
opened drawer or box. We include bin picking and placing because the bin
does not require additional actions to open.Box-open-v0Box-close-v0Bin-pick-v0Bin-place-v0Drawer-open-v0Drawer-close-v0Intermediate TaskThese tasks additionally require placing the object inside an open
drawer or box. We include theBin-pickingenvironment for
completeness.NameStatusBin-pick-v2✅ doneBin-place-v2✅ doneBox-place-v2✅ doneBox-pick-v2✅ doneDrawer-place-v2✅ doneDrawer-pick-v2✅ doneBin-pick-v0Bin-place-v0Box-pick-v0Box-place-v0Drawer-pick-v0Drawer-place-v0Multi-task EnvironmentsThese environments require significantly more memory due to the
increasing complexity of contact detection and collision dynamics. These
are also slower to run.NameRenderBoxBin-v2✅ doneDrawerBin-v2✅ doneBoxBinDrawer-v2✅ doneBoxBin-v0DrawerBin-v0BoxBinDrawer-v0
Package: gym-games
# Gym GamesThis is a gym compatible version of various games for reinforcenment learning.For [PyGame Learning Environment](https://pygame-learning-environment.readthedocs.io/en/latest/user/games.html), the default observation is a non-visual state representation of the game.For [MinAtar](https://github.com/kenjyoung/MinAtar), the default observation is a visual input of the game.## EnvironmentsPyGame learning environment:
- Catcher-PLE-v0
- FlappyBird-PLE-v0
- Pixelcopter-PLE-v0
- PuckWorld-PLE-v0
- Pong-PLE-v0MinAtar:
- Asterix-MinAtar-v0
- Breakout-MinAtar-v0
- Freeway-MinAtar-v0
- Seaquest-MinAtar-v0
- Space_invaders-MinAtar-v0## Installation### GymPlease read the instruction [here](https://github.com/openai/gym).### PygameOn OSX:brew install sdl sdl_ttf sdl_image sdl_mixer portmidi
pip install pygameOn Ubuntu:sudo apt-get -y install python-pygame
pip install pygameOthers: Please read the instruction [here](http://www.pygame.org/wiki/GettingStarted#Pygame%20Installation).### PyGame Learning Environmentpip install git+https://github.com/ntasfi/PyGame-Learning-Environment.git## MinAtarpip install git+https://github.com/kenjyoung/MinAtar.git### Gym-gamesInstall from source:pip install git+https://github.com/qlan3/gym-games.gitInstall from PyPi:pip install gym-games## ExampleRunpython test.py.## CitePlease use this bibtex to cite this repo:@misc{gym-games,
author = {Qingfeng, Lan},
title = {Gym Compatible Games for Reinforcenment Learning},
year = {2019},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {url{https://github.com/qlan3/gym-games}}
}## References[gym](https://github.com/openai/gym/tree/master/)[gym-ple](https://github.com/lusob/gym-ple)[SRNN](https://github.com/VincentLiu3/SRNN)[MinAtar](https://github.com/kenjyoung/MinAtar)
Package: gym-gopherfx
gym-gopherfxA gym environment for reinforcement training on forex historical data.Historical data is no way a predictor of future behaviour in forex, but can be used as challenging science experiment :).How to runClone the repodocker-composeup-dAccesshttp://127.0.0.1:8050/to access the dashboard.To start a random agent and see live evolutions:pythonrandom_agent.pyAbout the environmentGopherfx-v0Observation:
A tuple of the current datetime and rateThe available actions are:0 - wait1 - buy2 - sellReward:0 - for waiting/first buy/first selltrade_result - for closing an open position-1 - when depleting the budgetThe episodes ends when the trading day is over or the budget is depleted.Gopherfx-v1Observation:A tuple of Candle rate data representing the market state at the step momentThe value for the current open positionThe action required to close the current open positionRaw example:{"volume":45,"time":"2018-06-01T00:00:00.000000000Z","bid":{"o":"1.16916","h":"1.16929","l":"1.16908","c":"1.16915"},"ask":{"o":"1.16933","h":"1.16946","l":"1.16924","c":"1.16930"}}The available actions are:0 - wait1 - buy2 - sellReward:0 - for waiting/first buy/first selltrade_result - for closing an open position-1 - when depleting the budgetThe episode ends when the trading day is over or the budget is depleted.
Package: gymgrid
GymgridThe gridworld environment contains simple environments in RL book and compatible with OpenAI-gym.Installationpip install gymgirdUsageimport gym
import gymgrid
env = env = gym.make('cliff-v0')
for i_episode in range(20):
    observation = env.reset()
    for t in range(100):
        env.render()
        print(observation)
        action = env.action_space.sample()
        observation, reward, done, info = env.step(action)
        if done:
            print("Episode finished after {} timesteps".format(t+1))
            break
env.close()
print("env closed")EnvironmentsSample1Sample2Cliff: Example 6.5: Cliff WalkingWindyGridWorld: Exercise 6.9 Windy Gridworld with King's Moves
Package: gymgrid2
GymgridThis is a fork fromhttps://github.com/wsgdrfz/gymgrid. The original implementation does no longer work in the latest versions of gym due to the change from the pyglet renderer to pygame.The gridworld environment contains simple environments in RL book and compatible with OpenAI-gym.Installationpip install gymgrid2Usageimport gym
import gymgrid2
env = gym.make('cliff-v0')
for i_episode in range(20):
    observation = env.reset()
    for t in range(100):
        env.render()
        print(observation)
        action = env.action_space.sample()
        observation, reward, done, info = env.step(action)
        if done:
            print("Episode finished after {} timesteps".format(t+1))
            break
env.close()
print("env closed")EnvironmentsSample1Sample2Cliff: Example 6.5: Cliff WalkingWindyGridWorld: Exercise 6.9 Windy Gridworld with King's Moves
Package: gym-gridworlds
Implementation of three gridworlds environments
from bookReinforcement Learning: An Introductioncompatible withOpenAI gym.Usage$ import gym
$ import gym_gridworlds
$ env = gym.make('Gridworld-v0')  # substitute environment's nameGridworld-v0Gridworld is simple 4 times 4 gridworld from example 4.1 in the [book].
There are fout action in each state (up, down, right, left)
which deterministically cause the corresponding state transitions
but actions that would take an agent of the grid leave a state unchanged.
The reward is -1 for all tranistion until the terminal state is reached.
The terminal state is in top left and bottom right coners.WindyGridworld-v0Windy gridworld is from example 6.5 in thebook.
Windy gridworld is a standard gridworld as described above
but there is a crosswind upward through the middle of the grid.
Action are standard but in the middle region the resultant states are
shifted upward by a wind which strength varies between columns.Cliff-v0Cliff walking is a gridworld example 6.6 from thebook.
Again reward is -1 on all transition except those into region
that is cliff.
Stepping into this region incurs a reward of -100
and sends the agent instantly back to the start.