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oog 1.1

Object-Oriented Games

Description
Project Website
Summary
This Object-Oriented Games (OOG)
library is a general-purpose
python-based platform for interactive games. It aims to satisfy the following
criteria:

Highly customizable. Environment physics, reward structure, agent interface,
and more are customizable.
Easy to rapidly prototype tasks. Tasks can be composed in a single short
file.
Usable for both reinforment learning and psychology, with DeepMind
dm_env and OpenAI
Gym interfaces for RL agents and an
MWorks interface for psychology and
neurophysiology.
Light-weight and efficient. Most tasks run quickly, almost always faster than
100 frames per second on CPU and often much faster than that.
Facilitates procedural generation for randomizing task conditions every trial.

See oog_demos for a variety of example tasks.
Intended Users
OOG may be useful for the following kinds of researchers:

Machine learning researchers studying reinforcement learning in
2.5-dimensional physical environments who want to quickly implement tasks
without having to wrangle with more complicated game engines that aren't
designed for RL.
Psychology researchers who want more flexibility than existing psychology
platforms afford.
Neurophysiology researchers who want to study interactive games yet still need
to precisely control stimulus timing.
Machine learning researchers studying unsupervised learning, particularly in
the video domain. OOG can be used to procedurally generate video datasets with
controlled statistics.

Introduction
The core philosophy of OOG is "one task, one file." Namely, each task can be
implemented with a single configuration file. This configuration file should be
a short "recipe" for the task, containing as little substantive code as
possible, and should define a set of components to pass to the OOG environment.
See the OOG README for more details.
We also include an example MWorks interface for
running psychophysics experiments, as well as a python demo
script for testing task prototypes.
Features Compared to Existing Platforms
Compared to professional game engines (Unity, Unreal, etc.) and existing visual
reinforcement learning platforms (DM-Lab, Mujoco, VizDoom, etc.):

Python. OOG tasks are written purely in python, so users who are most
comfortable with python will find OOG easy to use.
Procedural Generation. OOG facilitates procedural generation, with a
library of compositional
distributions to randomize
conditions across trials.
Online Simulation. OOG supports online model-based RL, with a ground truth
simulator for tree search.
Psychophysics. OOG can be run with MWorks, a psychophysics platform.
Speed. OOG is fast on CPU. While the speed depends on the task and
rendering resolution, OOG typically runs at ~200fps with 512x512 resolution on
a CPU, which is faster than one would get with DM-Lab or Mujoco and at least
as fast as Unity and Unreal.

Compared to existing python game platforms (PyBullet, Pymunk, etc.):

Customization. Custom forces and game rules can be easily implemented in
OOG.
Psychophysics, Procedural Generation, and Online Simulation, as described
above.
RL Interface. A task implemented in OOG can be used out-of-the-box to
train RL agents, since OOG is python-based and has DeepMind dm_env and OpenAI
Gym interfaces.

Compared to existing psychophysics platforms (PsychoPy, PsychToolbox, MWorks):

Flexibility. OOG offers a large scope of interactive tasks. Existing psychophysics platforms are not easily customized for
game-like tasks, action interfaces, and arbitrary object shapes.
Physics. Existing psychophysics platforms do not have built-in physics,
such as forces, collisions, etc.
RL Interface, as described above.

OOG can interface with MWorks, allowing users to leverage the OOG task
framework while also allowing for precise timing control and interfaces with
eye-trackers, joysticks, and electrophysiology software.
Limitations

Not 3D. OOG environments are 2.5-dimensional, meaning that they render in
2-dimensions with z-ordering for occlusion. OOG does not support 3D sprites.
Very simple graphics. OOG sprites are monochromatic polygons. There are no
textures, shadows, or other visual effects. Composite sprites can be
implemented by creating multiple overlapping sprites, but still the graphics
complexity is very limited. This has the benefit of a small and easily
parameterizable set of factors of variation of the sprites, but does make OOG
environments visually unrealistic.
Imperfect physics. OOG's physics engine is simple. It uses Newton's method
to effect action-at-a-distance forces. OOG does include a collision module
that implements rotational mechanics, but it is not as robust as more
professional physics engines and can have instabilities (particularly if
multiple objects collide simultaneously). See
oog_demos/example_configs/falling_balls.py
for an extreme example of unstable physics.

Getting Started
See the project website for
API documentation about every file and function in OOG.
Installation
If you would like to install this library as a package, you can install using
pip:
pip install oog

This will install oog and oog_demos packages. Be sure to use python 3.7 or
later.
Running The Demo
Tasks can be played by running the run_demo script, in which the
--config flag indicates the task config to demo. For example, to demo the
pong task, you would run:
python3 -m oog_demos.run_demo --config='oog_demos.example_configs.pong'

When this command is run, the demo will produce an interactive display. At the
top of the display is the rendered environment state, in the middle of the
display is a histogram of recent rewards, and at the bottom of the display is a
top-down view of a cartoon joystick. You can click and drag the joystick around
to control the agent avatar. The demo can be terminated by pressing escape.
The pong task looks like this:

You can change the config flag to point to any of the example
configs. They will all run except for cleanup,
which is multi-agent so cannot be played by a single-agent demo, (though see
multi_agent_example for more about that).
Implementing Tasks
Before implementing your own tasks, please read the OOG README.
To begin implementing your own task, we recommend first looking at all the
example configs in oog_demos and copying one with some
similarities to your task into a working directory. Then modify it incrementally
to your specification.
To demo your config, copy run_demo.py into your
working directory and run it with
$ python3 run_demo.py --config='path.to.your.config' --level=$your_config_level

Contact and Support
Please see CONTRIBUTING.md for information about support.
Please email Nick Watters at nwatters@mit.edu with questions and feedback.
Reference
Some parts of this codebase are derived from
Spriteworld. See the Spriteworld
license in LICENSE-spriteworld.