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jerryz123 committed Jul 5, 2018
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5 changes: 1 addition & 4 deletions .gitignore
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*.model
*.pickle
*.eps
*.pdf
data/
temp/
test_data/
*.model
*.pickle
*.pdf
*.eps
*.png

20 changes: 20 additions & 0 deletions docs/Makefile
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# Minimal makefile for Sphinx documentation
#

# You can set these variables from the command line.
SPHINXOPTS =
SPHINXBUILD = sphinx-build
SPHINXPROJ = UrbanDrivingSimulator
SOURCEDIR = source
BUILDDIR = build

# Put it first so that "make" without argument is like "make help".
help:
@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)

.PHONY: help Makefile

# Catch-all target: route all unknown targets to Sphinx using the new
# "make mode" option. $(O) is meant as a shortcut for $(SPHINXOPTS).
%: Makefile
@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
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180 changes: 180 additions & 0 deletions docs/source/conf.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# Urban Driving Simulator documentation build configuration file, created by
# sphinx-quickstart on Mon Nov 13 01:10:49 2017.
#
# This file is execfile()d with the current directory set to its
# containing dir.
#
# Note that not all possible configuration values are present in this
# autogenerated file.
#
# All configuration values have a default; values that are commented out
# serve to show the default.

# If extensions (or modules to document with autodoc) are in another directory,
# add these directories to sys.path here. If the directory is relative to the
# documentation root, use os.path.abspath to make it absolute, like shown here.
#
import os
import sys
sys.path.insert(0, os.path.abspath('../../'))
print(sys.path)

# -- General configuration ------------------------------------------------

# If your documentation needs a minimal Sphinx version, state it here.
#
# needs_sphinx = '1.0'

# Add any Sphinx extension module names here, as strings. They can be
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = ['sphinx.ext.coverage',
'sphinx.ext.mathjax',
'sphinx.ext.viewcode',
'sphinx.ext.githubpages',
'sphinx.ext.autodoc',
'sphinx.ext.napoleon']

# Add any paths that contain templates here, relative to this directory.
templates_path = ['_templates']

# The suffix(es) of source filenames.
# You can specify multiple suffix as a list of string:
#
# source_suffix = ['.rst', '.md']
from recommonmark.parser import CommonMarkParser

source_parsers = {
'.md': CommonMarkParser,
}

source_suffix = ['.rst', '.md']

# The master toctree document.
master_doc = 'index'

# General information about the project.
project = 'FLUIDS - First-order Lightweight Urban Intersection Driving Simulator'
copyright = '2018, Jerry Zhao, Andy Cui, Michael Laskey, Berkeley AUTOLAB'
author = 'Jerry Zhao, Andrew Cui, Michael Laskey, Berkeley AUTOLAB'

# The version info for the project you're documenting, acts as replacement for
# |version| and |release|, also used in various other places throughout the
# built documents.
#
# The short X.Y version.
version = '2.0'
# The full version, including alpha/beta/rc tags.
release = '2.0'

# The language for content autogenerated by Sphinx. Refer to documentation
# for a list of supported languages.
#
# This is also used if you do content translation via gettext catalogs.
# Usually you set "language" from the command line for these cases.
language = None

# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This patterns also effect to html_static_path and html_extra_path
exclude_patterns = []

# The name of the Pygments (syntax highlighting) style to use.
pygments_style = 'sphinx'

# If true, `todo` and `todoList` produce output, else they produce nothing.
todo_include_todos = False


# -- Options for HTML output ----------------------------------------------

# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
#
html_theme = 'sphinx_rtd_theme'

# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
#
# html_theme_options = {}

# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
html_static_path = ['_static']

# Custom sidebar templates, must be a dictionary that maps document names
# to template names.
#
# This is required for the alabaster theme
# refs: http://alabaster.readthedocs.io/en/latest/installation.html#sidebars
html_sidebars = {
'**': [
'relations.html', # needs 'show_related': True theme option to display
'searchbox.html',
]
}


# -- Options for HTMLHelp output ------------------------------------------

# Output file base name for HTML help builder.
htmlhelp_basename = 'FLUIDSdoc'


# -- Options for LaTeX output ---------------------------------------------

latex_elements = {
# The paper size ('letterpaper' or 'a4paper').
#
# 'papersize': 'letterpaper',

# The font size ('10pt', '11pt' or '12pt').
#
# 'pointsize': '10pt',

# Additional stuff for the LaTeX preamble.
#
# 'preamble': '',

# Latex figure (float) alignment
#
# 'figure_align': 'htbp',
}

# Grouping the document tree into LaTeX files. List of tuples
# (source start file, target name, title,
# author, documentclass [howto, manual, or own class]).
latex_documents = [
(master_doc, 'FLUIDS.tex', 'FLUIDS Documentation',
'Jerry Zhao, Andrew Cui, Michael Laskey, Berkeley AUTOLAB', 'manual'),
]


# -- Options for manual page output ---------------------------------------

# One entry per manual page. List of tuples
# (source start file, name, description, authors, manual section).
man_pages = [
(master_doc, 'FLUIDS', 'FLUIDS Documentation',
[author], 1)
]


# -- Options for Texinfo output -------------------------------------------

# Grouping the document tree into Texinfo files. List of tuples
# (source start file, target name, title, author,
# dir menu entry, description, category)
texinfo_documents = [
(master_doc, 'UrbanDrivingSimulator', 'FLUIDS Documentation',
author, 'FLUIDS', 'One line description of project.',
'Miscellaneous'),
]



41 changes: 41 additions & 0 deletions docs/source/core_api.rst
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FLUIDS Core API
===============

Simulator Interface
^^^^^^^^^^^^^^^^^^^

The most powerful way to interact with fluids is to create a ``fluids.FluidSim`` object. This object creates the environment, sets up all cars, and pedestrians, and controls background objects in the scene. The initialization arguments to this object control the parameters of the generated environment

.. autoclass:: fluids.FluidSim
:members: get_control_keys, step

Action Types
^^^^^^^^^^^^
FLUIDS supports four action types. All action types are acceptable for ``FluidSim.step``.

.. autoclass:: fluids.actions.KeyboardAction
.. autoclass:: fluids.actions.SteeringAction
.. autoclass:: fluids.actions.VelocityAction
.. autoclass:: fluids.actions.LastValidAction


Command Line Interface
^^^^^^^^^^^^^^^^^^^^^^
FLUIDS also provides a command line interface for visualizing the environment running without a user agent. Controlled agents in this mode are controllable by keyboard only.

::

python3 -m fluids

Run with -h flag to see arguments

::
>>> python3 -m fluids -h
optional arguments:
-h, --help show this help message and exit
-b N Number of background cars
-c N Number of controlled cars
-p N Number of background pedestrians
-v N Visualization level
--state layout file Layout file for state generation
15 changes: 15 additions & 0 deletions docs/source/gym_api.rst
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FLUIDS Gym Env API
==================

The FLUIDS Gym Environment provides a simpler common interface to the FLUIDS simulator that is compatible with general agents following the Gym API.


::

import gym
import gym_fluids

env = gym.make("fluids-v2")


The current Gym environment supports 1 controlled car interacting with 10 background cars and 5 pedestrians. The action space is a ``[steering, acc]`` pair. The observation space is a ``500 x 500`` RGB image from the car's perspective. A reward function considering collisions and distance traveled along the given trajectory is provided.
15 changes: 15 additions & 0 deletions docs/source/index.rst
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FLUIDS - A First-Order Lightweight Urban Intersection Driving Simulator
=======================================================================

FLUIDS - a First-Order Local Urban Intersection Driving Simulator by Berkeley AUTOLAB.
:download:`Read our paper <case2018_final.pdf>`


.. toctree::
:maxdepth: 2
:caption: Contents:

why_fluids.rst
installation.rst
core_api.rst
gym_api.rst
25 changes: 25 additions & 0 deletions docs/source/installation.rst
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Installation
============

Installing FLUIDS Core
^^^^^^^^^^^^^^^^^^^^^^

The FLUIDS core simulator provides the core simulation environment. Currently only install from source is supported.

::

git clone https://github.com/BerkeleyAutomation/Urban_Driving_Simulator.git
cd Urban_Driving_Simulator
git checkout v2
pip3 install -e .

Installing FLUIDS Gym Environments
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

The FLUIDS Gym Environment is compatible with agents designed for OpenAI's Gym benchmark API. This interface layer depends on FLUIDS core.

::
git clone https://github.com/jerryz123/gym_fluids.git
cd gym_fluids
pip3 install -e .
18 changes: 18 additions & 0 deletions docs/source/why_fluids.rst
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Why FLUIDS?
=============
To study and compare Reinforcement and Imitation Learning algorithms, the most commonly used benchmarks are OpenAI Gym, Mujoco, and ATARI games. However, these benchmarks generally fail to capture real-life challenges to learning algorithms, including multi-agent interactions, noisy sensors, and generalization. FLUIDS aims to fill this gap by providing a fast, birds-eye simulation of cars and pedestrians in an urban driving setting. Below, we highlight several notable capabilities of FLUIDS.

Tests Generalization
^^^^^^^^^^^^^^^^^^^^^^^
FLUIDS is designed to test how well agents generalize to new environments that are both in and out of sample from the initial state distribution. The initial state distribution can be specified to be a range of cars on the road starting at various lane positions. The randomness stems from the number of cars currently on the road and the location of each car.

To test how robust a policy is to out of sample distributions, FLUIDS allows for perturbations such as enabling pedestrians, varying traffic light timing and changing the amount of noise in the sensor readings.


Multi-Agent Planning
^^^^^^^^^^^^^^^^^^^^^
Another advantage of FLUIDS is that the number of supervisors that can be controlled by an agent and that controlled by the simulator is variable. Experiments such as coordinating a fleet of self-driving cars traversing an intersection or having a single self-driving car pass an intersection with multiple cars and pedestrians are all supported.

Built-in Supervisors
^^^^^^^^^^^^^^^^^^^^^
In order to collect consist training data for Imitation Learning experiments and a baseline for performance. FLUIDS provides access to an array of supervisors, which can perform the driving tasks by having access to the global state of the world. The algorithms for the supervisors are the same planning stack used for the background agents.
2 changes: 1 addition & 1 deletion fluids/__main__.py
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Expand Up @@ -11,7 +11,7 @@
help='Number of background pedestrians')
parser.add_argument('-v', metavar='N', type=int, default=1,
help='Visualization level')
parser.add_argument('--state', metavar='layout file', type=str, default=fluids.STATE_CITY,
parser.add_argument('--state', metavar='file', type=str, default=fluids.STATE_CITY,
help='Layout file for state generation')

args = parser.parse_args()
Expand Down
23 changes: 23 additions & 0 deletions fluids/actions.py
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Expand Up @@ -3,21 +3,44 @@ def get_action(self):
raise NotImplementedError

class KeyboardAction(Action):
"""
This action passes control to keyboard input
"""
pass

class SteeringAction(Action):
"""
This action provides both steering and acceleration control
Parameters
----------
steer: float
acc: float
"""

def __init__(self, steer, acc):
self.steer = steer
self.acc = acc
def get_action(self):
return self.steer, self.acc

class VelocityAction(Action):
"""
This action provides a target velocity for the car to track
Parameters
----------
vel: float
"""
def __init__(self, vel):
self.vel = vel
def get_action(self):
return self.vel

class LastValidAction(Action):
"""
This action causes car to replay its last valid action.
This is useful when testing coarse planning methods.
"""
def __init__(self):
pass

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