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Universe: a software platform for measuring and training an AI's general intelligence across the world's supply of games, websites and other applications.

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Quick Overview

OpenAI Universe is an open-source platform for measuring and training AI systems across a wide range of environments, including web browsers, games, and other applications. It provides a unified interface for interacting with these diverse environments, allowing researchers and developers to train AI agents on a variety of tasks.

Pros

  • Offers a diverse set of environments for AI training
  • Provides a standardized interface for interacting with different environments
  • Supports integration with popular deep learning frameworks
  • Enables easy benchmarking and comparison of AI algorithms

Cons

  • Some environments may require significant computational resources
  • Documentation can be sparse or outdated for certain components
  • Setup process can be complex for beginners
  • Limited active development and updates in recent years

Code Examples

import gym
import universe

# Create and initialize the environment
env = gym.make('flashgames.DuskDrive-v0')
env.configure(remotes=1)  # Create a remote VNC connection

# Reset the environment and get the initial observation
observation_n = env.reset()

# Take a random action in the environment
action_n = [[('KeyEvent', 'ArrowUp', True)] for _ in observation_n]
observation_n, reward_n, done_n, info = env.step(action_n)

This example demonstrates how to create and interact with a Flash game environment using Universe.

import gym
import universe

# Create a browser-based environment
env = gym.make('internet.SlitherIO-v0')
env.configure(remotes=1)

# Reset the environment
observation_n = env.reset()

# Perform a mouse click action
action_n = [[('PointerEvent', 200, 200, 0)] for _ in observation_n]
observation_n, reward_n, done_n, info = env.step(action_n)

This example shows how to interact with a browser-based environment and perform mouse actions.

Getting Started

To get started with OpenAI Universe, follow these steps:

  1. Install Universe and its dependencies:

    pip install gym universe

  2. Install additional dependencies for specific environments:

    pip install gym[atari]

  3. Create and run a simple environment:

    import gym
import universe

env = gym.make('flashgames.DuskDrive-v0')
env.configure(remotes=1)
observation_n = env.reset()

while True:
    action_n = [[('KeyEvent', 'ArrowUp', True)] for _ in observation_n]
    observation_n, reward_n, done_n, info = env.step(action_n)
    env.render()

This code creates a Flash game environment, performs actions, and renders the game state. Note that you may need to install additional dependencies depending on the specific environments you want to use.

Competitor Comparisons

google-deepmind logo

open_spiel

4,162

OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.

Pros of Open Spiel

  • Focuses on reinforcement learning in games, providing a comprehensive suite of game environments
  • Offers a wide variety of game types, including imperfect information games
  • Actively maintained with regular updates and contributions

Cons of Open Spiel

  • More specialized in scope, primarily targeting game-based environments
  • May have a steeper learning curve for researchers not familiar with game theory concepts

Code Comparison

Open Spiel:

import pyspiel

game = pyspiel.load_game("tic_tac_toe")
state = game.new_initial_state()
while not state.is_terminal():
    legal_actions = state.legal_actions()
    action = legal_actions[0]
    state.apply_action(action)

Universe:

import gym
import universe

env = gym.make('flashgames.DuskDrive-v0')
observation_n = env.reset()
while True:
    action_n = [[('KeyEvent', 'ArrowUp', True)] for _ in observation_n]
    observation_n, reward_n, done_n, info = env.step(action_n)

Both repositories provide environments for reinforcement learning, but Open Spiel focuses on game-theoretic scenarios, while Universe offers a broader range of environments, including web-based games and applications. Open Spiel's code is more game-specific, while Universe provides a more general-purpose interface for various tasks.

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gym

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A toolkit for developing and comparing reinforcement learning algorithms.

Pros of Gym

  • Simpler and more focused on reinforcement learning environments
  • Actively maintained and regularly updated
  • Widely adopted in the RL research community

Cons of Gym

  • Limited to reinforcement learning tasks
  • Doesn't provide as wide a range of environments as Universe
  • Less suitable for testing AI agents in real-world scenarios

Code Comparison

Gym:

import gym
env = gym.make('CartPole-v1')
observation, info = env.reset(seed=42)
for _ in range(1000):
    action = env.action_space.sample()
    observation, reward, terminated, truncated, info = env.step(action)

Universe:

import gym
import universe
env = gym.make('flashgames.DuskDrive-v0')
observation_n = env.reset()
while True:
    action_n = [[('KeyEvent', 'ArrowUp', True)] for _ in observation_n]
    observation_n, reward_n, done_n, info = env.step(action_n)

Universe offers a broader range of environments, including real-world applications like web browsers and desktop interfaces. Gym focuses on classic RL problems and simulated environments. Universe's code tends to be more complex due to its wider scope, while Gym's API is more straightforward for standard RL tasks.

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ml-agents

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The Unity Machine Learning Agents Toolkit (ML-Agents) is an open-source project that enables games and simulations to serve as environments for training intelligent agents using deep reinforcement learning and imitation learning.

Pros of ml-agents

  • Tightly integrated with Unity game engine, allowing for easy setup and deployment of AI agents in Unity projects
  • Provides a more comprehensive toolkit for training agents, including curriculum learning and imitation learning
  • Offers better documentation and tutorials for beginners

Cons of ml-agents

  • Limited to Unity environment, less versatile for non-game applications
  • Requires knowledge of Unity and C# for advanced usage
  • Smaller community compared to Universe, potentially leading to fewer resources and third-party integrations

Code Comparison

ml-agents:

public class RollerAgent : Agent
{
    public override void OnEpisodeBegin()
    {
        // Reset agent position and state
    }
}

Universe:

import gym
env = gym.make('flashgames.DuskDrive-v0')
observation = env.reset()
action = env.action_space.sample()

The code snippets demonstrate the different approaches to agent initialization and environment setup. ml-agents uses C# and is tightly integrated with Unity's component system, while Universe uses Python and provides a more generic interface for various environments.

google-research logo

football

3,305

Check out the new game server:

Pros of Football

  • Focused on a specific domain (soccer), allowing for more specialized and in-depth research
  • Provides a more realistic and complex environment for multi-agent reinforcement learning
  • Offers built-in support for curriculum learning and customizable scenarios

Cons of Football

  • Limited scope compared to Universe's diverse range of environments
  • Less flexibility for testing AI agents across different types of tasks
  • Smaller community and ecosystem due to its specialized nature

Code Comparison

Football:

env = football_env.create_environment(
    env_name="academy_3_vs_1_with_keeper",
    representation="raw",
    render=True
)

Universe:

env = gym.make('flashgames.DuskDrive-v0')
env.configure(remotes=1)
observation_n = env.reset()

Both repositories provide environments for reinforcement learning, but Football focuses on soccer simulations while Universe offers a broader range of environments. Football's code is more specific to its domain, while Universe's code is more generic and adaptable to various tasks.

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PARL

3,240

A high-performance distributed training framework for Reinforcement Learning

Pros of PARL

  • More active development and maintenance
  • Broader support for reinforcement learning algorithms
  • Better documentation and tutorials for beginners

Cons of PARL

  • Less focus on environment variety compared to Universe
  • Primarily centered around PaddlePaddle framework, potentially limiting flexibility

Code Comparison

PARL example:

import parl
from parl import layers

class Model(parl.Model):
    def __init__(self, act_dim):
        self.fc1 = layers.fc(size=128, act='relu')
        self.fc2 = layers.fc(size=act_dim)

Universe example:

import gym
import universe

env = gym.make('flashgames.DuskDrive-v0')
observation_n = env.reset()

while True:
    action_n = [[('KeyEvent', 'ArrowUp', True)] for _ in observation_n]
    observation_n, reward_n, done_n, info = env.step(action_n)

Summary

PARL offers a more comprehensive reinforcement learning toolkit with better documentation, while Universe focuses on providing a wide range of environments for AI training. PARL is more actively maintained but may be less flexible due to its PaddlePaddle integration. Universe provides a broader range of environments but has seen less recent development.

microsoft logo

AirSim

16,254

Open source simulator for autonomous vehicles built on Unreal Engine / Unity, from Microsoft AI & Research

Pros of AirSim

  • More focused on realistic simulation for autonomous vehicles and drones
  • Provides high-fidelity visual and physics simulation
  • Integrates well with Unreal Engine for advanced graphics capabilities

Cons of AirSim

  • Narrower scope, primarily focused on autonomous systems and robotics
  • Steeper learning curve due to its complexity and integration with Unreal Engine
  • Less diverse range of environments compared to Universe's broad collection

Code Comparison

AirSim (Python API example):

import airsim

client = airsim.MultirotorClient()
client.enableApiControl(True)
client.armDisarm(True)
client.takeoffAsync().join()
client.moveToPositionAsync(-10, 10, -10, 5).join()

Universe (Python example):

import gym
import universe

env = gym.make('flashgames.DuskDrive-v0')
observation_n = env.reset()
while True:
    action_n = [[('KeyEvent', 'ArrowUp', True)] for _ in observation_n]
    observation_n, reward_n, done_n, info = env.step(action_n)

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README

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 <https://openai.com/blog/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 <https://github.com/openai/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 <https://openai.com/blog/universe/#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 <http://deepdrive.io/>_ and NVIDIA), but these aren't included in today's release.

.. contents:: Contents of this document :depth: 2

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 <http://conda.pydata.org/docs/using/envs.html>`__
before getting started, to keep all your Universe-related packages in the same place.

Install Universe
~~~~~~~~~~~~~~~~
To get started, first install ``universe``:

.. code:: shell

    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:

.. code:: shell

    pip install numpy
    sudo apt-get install golang libjpeg-turbo8-dev make

On Ubuntu 14.04:

.. code:: shell

    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:

.. code:: shell

    xcode-select --install

Or ``numpy``, ``libjpeg-turbo`` and ``incremental`` packages:

.. code:: shell

    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
<https://docs.docker.com/engine/installation/>`__ (on OSX, we
recommend `Docker for Mac
<https://docs.docker.com/docker-for-mac/>`__). You should be able to
run ``docker ps`` and get something like this:

.. code:: shell

     $ 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:

.. code:: shell

git clone https://github.com/openai/universe.git
cd universe

Build a docker image, tag it as 'universe':

.. code:: shell

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:

.. code:: shell

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 probably want to do another volume map from the universe repo on your host into the container, then shell in interactively:

.. code:: shell

docker run --privileged --rm -it -e DOCKER_NET_HOST=172.17.0.1 -v /var/run/docker.sock:/var/run/docker.sock -v (full path to cloned repo above):/usr/local/universe universe python

As you edit the files in your cloned git repo, they will be changed in your docker container and you'll be able to run them in python.

Note if you are using docker for Windows, you'll need to enable the relevant shared drive for this to work.

Notes on installation


* When installing ``universe``, you may see ``warning`` messages.  These lines occur when installing numpy and are normal.
* You'll need a ``go version`` of at least 1.5. Ubuntu 14.04 has an older Go, so you'll need to `upgrade <https://golang.org/doc/install>`_ your Go installation.
* We run Python 3.5 internally, so the Python 3.5 variants will be much more thoroughly performance tested. Please let us know if you see any issues on 2.7.
* While we don't officially support Windows, we expect our code to be very close to working there. We'd be happy to take pull requests that take our Windows compatibility to 100%. In the meantime, the easiest way for Windows users to run universe is to use the alternate configuration described above.

System overview
---------------

A Universe **environment** is similar to any other Gym environment:
the agent submits actions and receives observations using the ``step()``
method.

Internally, a Universe environment consists of two pieces: a **client** and a **remote**:

* The **client** is a `VNCEnv
  <https://github.com/openai/universe/blob/master/universe/envs/vnc_env.py>`_
  instance which lives in the same process as the agent. It performs
  functions like receiving the agent's actions, proxying them to the
  **remote**, queuing up rewards for the agent, and maintaining a
  local view of the current episode state.
* The **remote** is the running environment dynamics, usually a
  program running inside of a Docker container. It can run anywhere --
  locally, on a remote server, or in the cloud. (We have a separate
  page describing how to manage `remotes <doc/remotes.rst>`__.)
* The client and the remote communicate with one another using the
  `VNC <https://en.wikipedia.org/wiki/Virtual_Network_Computing>`__
  remote desktop system, as well as over an auxiliary WebSocket
  channel for reward, diagnostic, and control messages. (For more
  information on client-remote communication, see the separate page on
  the `Universe internal communication protocols
  <doc/protocols.rst>`__.)

The code in this repository corresponds to the **client** side of the
Universe environments. Additionally, you can freely access the Docker
images for the **remotes**. We'll release the source repositories for
the remotes in the future, along with tools to enable users to
integrate new environments. Please sign up for our `beta
<https://docs.google.com/forms/d/e/1FAIpQLScAiW-kIS0mz6hdzzFZJJFlXlicDvQs1TX9XMEkipNwjV5VlA/viewform>`_
if you'd like early access.

Run your first agent
--------------------

Now that you've installed the ``universe`` library, you should make
sure it actually works. You can paste the example below into your
``python`` REPL. (You may need to press enter an extra time to make
sure the ``while`` loop is executing.)

.. code:: python

  import gym
  import universe  # register the universe environments

  env = gym.make('flashgames.DuskDrive-v0')
  env.configure(remotes=1)  # automatically creates a local docker container
  observation_n = env.reset()

  while True:
    action_n = [[('KeyEvent', 'ArrowUp', True)] for ob in observation_n]  # your agent here
    observation_n, reward_n, done_n, info = env.step(action_n)
    env.render()

The example will instantiate a client in your Python process,
automatically pull the ``quay.io/openai/universe.flashgames`` image,
and will start that image as the remote. (In our `remotes
<doc/remotes.rst>`__ documentation page, we explain other ways you can run
remotes.)

It will take a few minutes for the image to pull the first time. After that,
if all goes well, a window like the one below will soon pop up. Your
agent, which is just pressing the up arrow repeatedly, is now
playing a Flash racing game called `Dusk Drive
<http://www.kongregate.com/games/longanimals/dusk-drive>`__. Your agent
is programmatically controlling a VNC client, connected to a VNC
server running inside of a Docker container in the cloud, rendering a
headless Chrome with Flash enabled:

.. image:: https://github.com/openai/universe/blob/master/doc/dusk-drive.png?raw=true
     :width: 600px

You can even connect your own VNC client to the environment, either
just to observe or to interfere with your agent. Our ``flashgames``
and ``gym-core`` images conveniently bundle a browser-based VNC
client, which can be accessed at
``http://localhost:15900/viewer/?password=openai``. If you're on Mac,
connecting to a VNC server is as easy as running: ``open
vnc://localhost:5900``.

(If using docker-machine, you'll need to replace "localhost" with the
IP address of your Docker daemon, and use ``openai`` as the password.)

Breaking down the example

So we managed to run an agent, what did all the code actually mean? We'll go line-by-line through the example.

  • First, we import the gym <https://github.com/openai/gym>__ library, which is the base on which Universe is built. We also import universe, which registers <https://github.com/openai/universe/blob/master/universe/__init__.py>__ all the Universe environments.

.. code:: python

import gym import universe # register the universe environments

  • Next, we create the environment instance. Behind the scenes, gym looks up the registration <https://github.com/openai/universe/blob/master/universe/__init__.py>__ for flashgames.DuskDrive-v0, and instantiates a VNCEnv <https://github.com/openai/universe/blob/master/universe/envs/vnc_env.py#L88>__ object which has been wrapped <https://github.com/openai/universe/blob/master/universe/wrappers/__init__.py#L42>__ to add a few useful diagnostics and utilities. The VNCEnv object is the client part of the environment, and it is not yet connected to a remote.

.. code:: python

env = gym.make('flashgames.DuskDrive-v0')

  • The call to configure() connects the client to a remote environment server. When called with configure(remotes=1), Universe will automatically create a Docker image running locally on your computer. The local client connects to the remote using VNC. (More information on client-remote communication can be found in the page on universe internal communication protocols <doc/protocols.rst>. More on configuring remotes is at remotes <doc/remotes.rst>.)

.. code:: python

env.configure(remotes=1)

  • When starting a new environment, you call env.reset(). Universe environments run in real-time, rather than stepping synchronously with the agent's actions, so reset is asynchronous and returns immediately. Since the environment will not have waited to finish connecting to the VNC server before returning, the initial observations from reset will be None to indicate that there is not yet a valid observation.

    Similarly, the environment keeps running in the background even if the agent does not call env.step(). This means that an agent that successfully learns from a Universe environment cannot take "thinking breaks": it must keep sending actions to the environment at all times.

    Additionally, Universe introduces the vectorized Gym API. Rather than controlling a single environment at a time, the agent can control a fixed-size vector of n environments, each with its own remote. The return value from reset is therefore a vector of observations. For more information, see the separate page on environment semantics <doc/env_semantics.rst>__)

.. code:: python

observation_n = env.reset()

  • At each step() call, the agent submits a vector of actions; one for each environment instance it is controlling. Each VNC action is a list of events; above, each action is the single event "press the ArrowUp key". The agent could press and release the key in one action by instead submitting [('KeyEvent', 'ArrowUp', True), ('KeyEvent', 'ArrowUp', False)] for each observation.

    In fact, the agent could largely have the same effect by just submitting ('KeyEvent', 'ArrowUp', True) once and then calling env.step([[] for ob in observation_n]) thereafter, without ever releasing the key using ('KeyEvent', 'ArrowUp', False). The browser running inside the remote would continue to statefully represent the arrow key as being pressed. Sending other unrelated keypresses would not disrupt the up arrow keypress; only explicitly releasing the key would cancel it. There's one slight subtlety: when the episode resets, the browser will reset, and will forget about the keypress; you'd need to submit a new ArrowUp at the start of each episode.

.. code:: python

action_n = [[('KeyEvent', 'ArrowUp', True)] for ob in observation_n]

  • After we submit the action to the environment and render one frame, step() returns a list of observations, a list of rewards, a list of "done" booleans indicating whether the episode has ended, and then finally an info dictionary of the form {'n': [{}, ...]}, in which you can access the info for environment i as info['n'][i].

    Each environment's info message contains useful diagnostic information, including latency data, client and remote timings, VNC update counts, and reward message counts.

.. code:: python

observation_n, reward_n, done_n, info = env.step(action_n)
env.render()
  • We call step in what looks like a busy loop. In reality, there is a Throttle <https://github.com/openai/universe/blob/master/universe/wrappers/__init__.py#L18>__ wrapper on the client which defaults to a target frame rate of 60fps, or one frame every 16.7ms. If you call it more frequently than that, step will sleep <https://github.com/openai/universe/blob/master/universe/wrappers/throttle.py>__ with any leftover time.

Testing

We are using pytest <http://doc.pytest.org/en/latest/>__ for tests. You can run them via:

.. code:: shell

pytest

Run pytest --help for useful options, such as pytest -s (disables output capture) or pytest -k <expression> (runs only specific tests).

Additional documentation

More documentation not covered in this README can be found in the doc folder <doc>__ of this repository.

Getting help

If you encounter a problem that is not addressed in this README page or in the extra docs <doc>, then try our wiki page of solutions to common problems <https://github.com/openai/universe/wiki/Solutions-to-common-problems> - and add to it if your solution isn't there!

You can also search through the issues <https://github.com/openai/universe/issues?utf8=%E2%9C%93&q=is%3Aissue>__ on this repository and our discussion board <https://discuss.openai.com/c/Universe>__ to see if another user has posted about the same problem or to ask for help from the community.

If you still can't solve your problem after trying all of the above steps, please post an issue on this repository.

What's next?

  • Get started training RL algorithms! You can try out the Universe Starter Agent <https://github.com/openai/universe-starter-agent>, an implementation of the A3C algorithm <https://arxiv.org/abs/1602.01783> that can solve several VNC environments.

  • For more information on how to manage remotes, see the separate documentation page on remotes <doc/remotes.rst>__.

  • Sign up for a beta <https://docs.google.com/forms/d/e/1FAIpQLScAiW-kIS0mz6hdzzFZJJFlXlicDvQs1TX9XMEkipNwjV5VlA/viewform>_ to get early access to upcoming Universe releases, such as tools to integrate new Universe environments or a dataset of recorded human demonstrations.

Changelog

  • 2017-02-08: The old location for wrappers.SafeActionSpace has been moved to wrappers.experimental.SafeActionSpace. SoftmaxClickMouse has also been moved to wrappers.experimental.SoftmaxClickMouse
  • 2017-01-08: The wrappers.SafeActionSpace has been moved to wrappers.experimental.SafeActionSpace. The old location will remain with a deprecation warning until 2017-02-08.
  • 2016-12-27: BACKWARDS INCOMPATIBILITY: The gym monitor is now a wrapper. Rather than starting monitoring as env.monitor.start(directory), envs are now wrapped as follows: env = wrappers.Monitor(env, directory). This change is on master and will be released with 0.21.0.