Merge pull request #28 from flow-project/amend_setup

Amend setup
- Created an environment.yml file that supersedes the rllab-multi-agent environment.yml file
- updated the setup instructions to handle it

Author: AboudyKreidieh

docs/source/flow_setup.rst

@@ -14,8 +14,11 @@ shell commands below to get started.
 Dependencies
 ============
 We begin by installing dependencies needed by the four repositories mentioned
-above. It will be useful to install `Anaconda <https://www.anaconda.com/download>`_
-for Python.
+above. **It is highly recommended that users install
+`Anaconda <https://www.anaconda.com/download>`_ or
+`Miniconda <https://conda.io/miniconda.html>`_
+for Python and the setup instructions will assume that you are 
+doing so.**
 
 For Ubuntu 16.04:
 ::
@@ -38,6 +41,7 @@ sumo
 Next, we install SUMO, an open source traffic microsimulator which will be used
 the update the states of vehicles, traffic lights, and other RL and
 human-driven agents during the simulation process.
+
 ::
 
     cd ~
@@ -47,21 +51,33 @@ human-driven agents during the simulation process.
     make -f Makefile.cvs
 
 If you have OSX, run the following commands
+
 ::
+
     export CPPFLAGS=-I/opt/X11/include
     export LDFLAGS=-L/opt/X11/lib
+    ./configure CXX=clang++ CXXFLAGS="-stdlib=libc++ -std=gnu++11" --with-xerces=/usr/local --with-proj-gdal=/usr/local
+    make -j$nproc
+    echo 'export SUMO_HOME="$HOME/sumo"' >> ~/.bash_profile
+    echo 'export PATH="$HOME/sumo/bin:$PATH"' >> ~/.bash_profile
+    echo 'export PYTHONPATH="$HOME/sumo/tools:$PYTHONPATH"' >> ~/.bash_profile
+    source ~/.bashprofile
+
+If you have Ubuntu 14.04+, run the following command
 
-Now for both OSX and linux run the following command
 ::
+
     ./configure
     make -j$nproc
     echo 'export SUMO_HOME="$HOME/sumo"' >> ~/.bashrc
     echo 'export PATH="$HOME/sumo/bin:$PATH"' >> ~/.bashrc
     echo 'export PYTHONPATH="$HOME/sumo/tools:$PYTHONPATH"' >> ~/.bashrc
     source ~/.bashrc
 
-Test your sumo install and version by running the following commands
+Finally, test your sumo install and version by running the following commands
+
 ::
+
     which sumo
     sumo --version
     sumo-gui
@@ -79,10 +95,19 @@ Ray/RLlib for more).
     cd ~
     git clone https://github.com/flow-project/flow.git
     cd flow
+    conda env create -f environment.yml
+    source activate flow
     python3 setup.py develop
+
+For linux run
+::
     echo 'export PYTHONPATH="$HOME/flow:$PYTHONPATH"' >> ~/.bashrc
     source ~/.bashrc
 
+For mac run
+::
+    echo 'export PYTHONPATH="$HOME/flow:$PYTHONPATH"' >> ~/.bash_profile
+    source ~/.bash_profile
 
 Testing the Installation
 ========================
@@ -121,9 +146,17 @@ to install the `rllab-multiagent` library, follow the below instructions
     cd rllab-multiagent
     conda env create -f environment.yml
     python3 setup.py develop
+
+For linux run
+::
     echo 'export PYTHONPATH="$HOME/rllab-multiagent:$PYTHONPATH"' >> ~/.bashrc
     source ~/.bashrc
 
+For mac run
+::
+    echo 'export PYTHONPATH="$HOME/rllab-multiagent:$PYTHONPATH"' >> ~/.bash_profile
+    source ~/.bash_profile
+
 Ray/RLlib (optional)
 ====================
 RLlib is another RL library that has been extensively tested on the Flow
@@ -150,7 +183,7 @@ Getting started (rllab-multiagent)
 To run any of the RL examples, make sure to run
 ::
 
-    source activate flow-rllab
+    source activate flow
     
 In order to test run an Flow experiment in rllab-multiagent, try the following
 command:
@@ -167,6 +200,11 @@ Getting started (Ray/RLlib)
 
 See `getting started with RLlib <http://ray.readthedocs.io/en/latest/rllib.html#getting-started>`_ for sample commands.
 
+To run any of the RL examples, make sure to run
+::
+
+    source activate flow
+
 In order to test run an Flow experiment in RLlib, try the following command:
 ::
 

environment.yml

@@ -0,0 +1,62 @@
+name: flow
+channels:
+    - https://conda.anaconda.org/kne 
+#    - https://conda.anaconda.org/tlatorre 
+    - https://conda.anaconda.org/cjs14
+    - https://conda.anaconda.org/menpo
+    - jjhelmus
+    - soumith
+dependencies:
+    - python==3.5.2
+    - numpy==1.13.3
+    - scipy==1.1.0
+    - path.py
+    - python-dateutil
+    - joblib==0.10.3
+    - mako==1.0.7
+    - ipywidgets==5.1.5
+    - numba==0.38.1
+    - flask==1.0.2
+    - pybox2d==2.3.1post2
+    - pygame==1.9.2a0
+    - h5py==2.8.0
+    - matplotlib==2.2.2
+    - opencv3=3.1.0
+    - scikit-learn==0.19.1
+    - mpi4py==2.0.0
+    - pandas==0.23.4
+    - pip:
+        - Pillow==4.2.1
+        - pyprind==2.11.2
+        - boto3==1.7.73
+        - PyOpenGL==3.1.0
+        - nose2==0.8.0
+        - pyzmq==17.1.0
+        - tqdm==4.24.0
+        - msgpack-python==0.5.6
+        - cached_property==1.4.3
+        - line_profiler==2.1.2
+        - cloudpickle==0.5.3
+        - Cython==0.28.5
+        - redis==2.10.6
+        - keras==1.2.1
+        - git+https://github.com/Theano/Theano.git@adfe319ce6b781083d8dc3200fb4481b00853791#egg=Theano
+        - git+https://github.com/neocxi/Lasagne.git@484866cf8b38d878e92d521be445968531646bb8#egg=Lasagne
+        - git+https://github.com/plotly/plotly.py.git@2594076e29584ede2d09f2aa40a8a195b3f3fc66#egg=plotly
+        - awscli==1.15.74
+        - gym==0.10.5
+        - pyglet==1.3.2
+        - git+https://github.com/neocxi/prettytensor.git
+        - jupyter==1.0.0
+        - progressbar2==3.38.0
+        - chainer==1.18.0
+        - tensorflow==1.10.0
+        - numpy-stl==2.2.0
+        - nibabel==2.1.0
+        - pylru==1.0.9
+        - hyperopt==0.1
+        - polling==0.3.0
+        - lxml==4.2.4
+        # RLLIB installs
+        - six==1.11.0
+        - redis==2.10.6

examples/rllab/stabilizing_the_ring.py

@@ -19,7 +19,7 @@
 
 
 def run_task(*_):
-    sumo_params = SumoParams(sim_step=0.1, sumo_binary="sumo-gui", seed=0)
+    sumo_params = SumoParams(sim_step=0.1, sumo_binary="sumo", seed=0)
 
     vehicles = Vehicles()
     vehicles.add(veh_id="rl",

requirements.txt

@@ -1,14 +1,15 @@
 gym==0.10.5
-numpy==1.14.5
+numpy==1.13.3
 scipy==1.1.0
 lxml==4.2.4
 # theano==0.8.2 # for rllab
 pyprind==2.11.2
 # lasagne # for rllab
-tensorflow==1.10.0
+tensorflow==1.8.0
 nose2==0.8.0
 six==1.11.0
 path.py
 joblib==0.10.3
 python-dateutil==2.7.3
 cached_property
+