Autoenv

`Document`	`Version`

This environment is transformed from ngsim_env and written in python.
This environment is an extensible environment that looks like rllab, we encourage any exploration on our environment, including but not limited to data source, feature extraction, action propagation, reward definition.
Added AGen algorithm

Installation:

installation instructions for AutoEnv

# install miniconda
wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh # Linux
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh # Mac
# answer yes to everything
sh ./Miniconda3-latest-Linux-x86_64.sh # Linux
sh ./Miniconda3-latest-MacOSX-x86_64.sh # Mac
# remove sh files
rm Miniconda3-latest-Linux-x86_64.sh # Linux
rm Miniconda3-latest-MacOSX-x86_64.sh  # Mac

source ~/.bashrc 
# if no bashrc file try ~/.bash_profile instead
conda install conda==4.6.14
cd ~
git clone https://github.com/JackFram/Autoenv.git
cd ~/Autoenv
conda env create -f environment.yml
conda env update
conda activate rllab3
# If hdf5 is not installed, install it as it is required by AutoEnvs later in the process
conda install hdf5
conda install tensorflow
cd ~/Autoenv
mkdir preprocessing/data
mkdir preprocessing/final_data
mkdir preprocessing/processed_data
mkdir preprocessing/lane

julia:

Install julia 1.1. Code snippet assuming you are installing julia to home directory. If not, please modify the path in bashrc step accordingly.

cd ~
wget https://julialang-s3.julialang.org/bin/linux/x64/1.1/julia-1.1.0-linux-x86_64.tar.gz
tar -xvf julia-1.1.0-linux-x86_64.tar.gz
rm julia-1.1.0-linux-x86_64.tar.gz
echo "export PATH=$(pwd)/julia-1.1.0/bin:$PATH" >> ~/.bashrc

Make sure the julia command pops up a julia 1.1 interpreter.

Install ngsim_env

If have problems for installing NGSIM package please check out this doc NGSIM contributed by Simin Liu

conda activate rllab3
git clone https://github.com/sisl/ngsim_env.git
cd ngsim_env

# enter a julia interpreter and install dependencies.
#   NOTE: I got some weird error with one of the packages, I think it was AutoViz
#   I just ignore the error and it seemed to work fine.
julia
  # Add dependencesjulia
  using Pkg
  Pkg.add(PackageSpec(url="https://github.com/sisl/Vec.jl"))
  Pkg.add(PackageSpec(url="https://github.com/sisl/Records.jl"))
  Pkg.add(PackageSpec(url="https://github.com/sisl/AutomotiveDrivingModels.jl"))
  Pkg.add(PackageSpec(url="https://github.com/sisl/AutoViz.jl"))
  Pkg.add(PackageSpec(url="https://github.com/sisl/AutoRisk.jl.git", rev="v0.7fixes"))
  Pkg.add(PackageSpec(url="https://github.com/sisl/NGSIM.jl.git"))
  Pkg.add(PackageSpec(url="https://github.com/sisl/BayesNets.jl.git"))

  # Add the local AutoEnvs module to our julia environment
  ] dev ~/ngsim_env/julia
 
  # make sure it works
  using NGSIM

cd ~/ngsim_env/julia/  
julia deps/build.jl

# make sure they work, by entering interpreter

julia
  using PyCall
  using PyPlot
  using HDF5
  exit()

add python interface

conda activate rllab3
cd ~/ngsim_env/python
python setup.py develop
pip install julia==0.2.0
# make sure it works:
python
  import julia
  julia.Julia()
  # if this not working try the commands pop up in the error information
  # or try
  from julia.api import Julia
  j = Julia(compiled_modules=False)
  # we just want to make sure it doesnt error
  exit()

replace package with our modified version unzip our modified packages and put directory under ~/.julia/packages/

cd ~/Autoenv/
rm -rf ~/.julia/packages/AutomotiveDrivingModels
rm -rf ~/.julia/packages/NGSIM
mv NGSIM ~/.julia/packages/
mv AutomotiveDrivingModels ~/.julia/packages/
julia 
    using AutomotiveDrivingModels
    using NGSIM

Automated running ( data preprocessing included )

First you need to put all your data under Autoenv/preprocessing/data/ your data should be the raw csv files categorized by time stamp directory ~/Autoenv/preprocessing/data
You might need to do some file path adjustment
Finally run one step code in project root directory ~/Autoenv

python adaption.py --n_proc 1 --params_filename itr_200.npz --use_multiagent True --n_envs 1 --adapt_steps 1

intelligent-control-lab / Autoenv