universe.openai.comGitHub - openai/universe: Universe: a software platform for measuring and training an AI's general i

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Sign up Universe: a software platform for measuring and training an AI's general intelligence across the world's supply of games, websites and other applications. https://universe.openai.com 210 commits 22 branches 0 packages 2 releases 23 contributors MIT Python Makefile Python 99.8% Makefile 0.2% Branch: master Find file Clone or download Clone with HTTPS Use Git or checkout with SVN using the web URL. Open in Desktop Download ZIP Downloading Want to be notified of new releases in openai/universe ? Sign in Sign up Launching GitHub Desktop If nothing happens, download GitHub Desktop and try again. Go back Launching GitHub Desktop If nothing happens, download GitHub Desktop and try again. Go back Launching Xcode If nothing happens, download Xcode and try again. Go back Launching Visual Studio If nothing happens, download the GitHub extension for Visual Studio and try again. Go back Latest commit gdb Update README Latest commit cc9ce6e Apr 5, 2018 Files Permalink Type Name Latest commit message Commit time Failed to load latest commit information. doc TLB bugfixes ( #135 ) Feb 1, 2017 example add wrappers.SoftmaxClickMouse Jan 28, 2017 tests/ functional Support gym without configure ( #162 ) Mar 28, 2017 universe Set OPENAI_REMOTE_VERBOSE=0 in env to quiet remote container logs ( #185 ) May 18, 2017 .dockerignore Actually use our code in tests Dec 21, 2016 .gitignore Avoid test for now. Dec 28, 2016 .travis.yml Only push new travis universe after successful builds on master ( #81 ) Dec 24, 2016 CODE_OF_CONDUCT.rst Add extra docs Dec 5, 2016 Dockerfile #15 Update main readme to assist Windows users with getting started ( #… Mar 31, 2017 ISSUE_TEMPLATE Formatting Jan 4, 2017 LICENSE Add extra docs Dec 5, 2016 Makefile Add Monitor Wrapper ( #79 ) Dec 24, 2016 README.rst Update README Apr 5, 2018 setup.py Fixes #171 Apr 5, 2017 test.dockerfile Actually use our code in tests Dec 21, 2016 tox.ini Support gym without configure ( #162 ) Mar 28, 2017 README.rst This repository has been deprecated in favor of the Retro ( https://github.com/openai/retro ) library. See our Retro Contest ( https://blog.openai.com/retro-contest ) blog post for detalis. universe Universe is a software platform for measuring and training an AI's general intelligence across the world's supply of games, websites and other applications. This is the universe open-source library, which provides a simple Gym interface to each Universe environment. Universe allows anyone to train and evaluate AI agents on an extremely wide range of real-time, complex environments. Universe makes it possible for any existing program to become an OpenAI Gym environment, without needing special access to the program's internals, source code, or APIs. It does this by packaging the program into a Docker container, and presenting the AI with the same interface a human uses: sending keyboard and mouse events, and receiving screen pixels. Our initial release contains over 1,000 environments in which an AI agent can take actions and gather observations. Additionally, some environments include a reward signal sent to the agent, to guide reinforcement learning. We've included a few hundred environments with reward signals. These environments also include automated start menu clickthroughs, allowing your agent to skip to the interesting part of the environment. We'd like the community's help to grow the number of available environments, including integrating increasingly large and complex games. The following classes of tasks are packaged inside of publicly-available Docker containers, and can be run today with no work on your part: Atari and CartPole environments over VNC: gym-core.Pong-v3 , gym-core.CartPole-v0 , etc. Flashgames over VNC: flashgames.DuskDrive-v0 , etc. Browser tasks ("World of Bits") over VNC: wob.mini.TicTacToe-v0 , etc. We've scoped out integrations for many other games, including completing a high-quality GTA V integration (thanks to Craig Quiter and NVIDIA), but these aren't included in today's release. Contents of this document universe Getting started Testing Additional documentation Getting help What's next? Getting started Installation Supported systems We currently support Linux and OSX running Python 2.7 or 3.5. We recommend setting up a conda environment before getting started, to keep all your Universe-related packages in the same place. Install Universe To get started, first install universe : git clone https://github.com/openai/universe.git cd universe pip install -e . If this errors out, you may be missing some required packages. Here's the list of required packages we know about so far (please let us know if you had to install any others). On Ubuntu 16.04: pip install numpy sudo apt-get install golang libjpeg-turbo8-dev make On Ubuntu 14.04: sudo add-apt-repository ppa:ubuntu-lxc/lxd-stable # for newer golang sudo apt-get update sudo apt-get install golang libjpeg-turbo8-dev make On OSX: You might need to install Command Line Tools by running: xcode-select --install Or numpy , libjpeg-turbo and incremental packages: pip install numpy incremental brew install golang libjpeg-turbo Install Docker The majority of the environments in Universe run inside Docker containers, so you will need to install Docker (on OSX, we recommend Docker for Mac ). You should be able to run docker ps and get something like this: $ docker ps CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES Alternate configuration - running the agent in docker The above instructions result in an agent that runs as a regular python process in your OS, and launches docker containers as needed for the remotes. Alternatively, you can build a docker image for the agent and run it as a container as well. You can do this in any operating system that has a recent version of docker installed, and the git client. To get started, clone the universe repo: git clone https://github.com/openai/universe.git cd universe Build a docker image, tag it as 'universe': docker build -t universe . This may take a while the first time, as the docker image layers are pulled from docker hub. Once the image is built, you can do a quick run of the test cases to make sure everything is working: docker run --privileged --rm -e DOCKER_NET_HOST=172.17.0.1 -v /var/run/docker.sock:/var/run/docker.sock universe pytest Here's a breakdown of that command: docker run - launch a docker container --rm - delete the container once the launched process finishes -e DOCKER_NET_HOST=172.17.0.1 - tells the universe remote (when launched) to make its VNC connection back to this docker-allocated IP -v /var/run/docker.sock:/var/run/docker.sock - makes the docker unix socket from the host available to the container. This is a common technique used to allow containers to launch other containers alongside itself. universe - use the imaged named 'universe' built above pytest - run 'pytest' in the container, which runs all the tests At this point, you'll see a bunch of tests run and hopefully all pass. To do some actual development work, you p...