A Virtual Training Gym for Autonomous Vehicle using Deep Reinforcement Learning
You can find the presentation sildes here.
The RoboGym package works on Linux, Mac and Windows. Use the following instruction to install the required packages and start the training,
In the terminal, First create and activate conda virtual environment as:
```
conda create -n RoboGym python=3.6
conda activate RoboGym
```
Secondly, in directory /Packages use following command:
```
pip install -e .
```
Third, in directory /Robogym_training/animalai_train , use the following command:
```
pip install -e .
```
Finally download the environment for your system:
| OS | Environment link |
|---|---|
| Linux | download v1.0.0 |
| MacOS | download v1.0.0 |
| Windows | download v1.0.0 |
You can now unzip the content of the archive to the ~/RoboGym/Env folder and you're ready to go! Make sure the executable
RoboGym.* is in ``~/RoboGym/Env. On linux you may have to make the file executable by running chmod +x env/RoboGym.x86_64`.
Then you can simply start training on your local machine running following command:
```sh
$ cd ~/RoboGym/RoboGym_training
$ python RoboGym.py
```
This section is a summary of practical sections from here
This page contains instructions for setting up an EC2 instance (using deep learning AMI on Amazon Web Service for training ML-Agents environments).
```sh
# Install Xorg
$ sudo apt-get update
$ sudo apt-get install -y xserver-xorg mesa-utils
$ sudo nvidia-xconfig -a --use-display-device=None --virtual=1280x1024
# Get the BusID information
$ nvidia-xconfig --query-gpu-info
# Add the BusID information to your /etc/X11/xorg.conf file
$ sudo sed -i 's/ BoardName "Tesla K80"/ BoardName "Tesla K80"\n BusID "0:30:0"/g' /etc/X11/xorg.conf
# Remove the Section "Files" from the /etc/X11/xorg.conf file
# And remove two lines that contain Section "Files" and EndSection
$ sudo vim /etc/X11/xorg.conf
```
```sh
# Download and install the latest Nvidia driver for ubuntu
# Please refer to http://download.nvidia.com/XFree86/Linux-#x86_64/latest.txt
$ wget http://download.nvidia.com/XFree86/Linux-x86_64/390.87/NVIDIA-Linux-x86_64-390.87.run
$ sudo /bin/bash ./NVIDIA-Linux-x86_64-390.67.run --accept-license --no-questions --ui=none
# Disable Nouveau as it will clash with the Nvidia driver
$ sudo echo 'blacklist nouveau' | sudo tee -a /etc/modprobe.d/blacklist.conf
$ sudo echo 'options nouveau modeset=0' | sudo tee -a /etc/modprobe.d/blacklist.conf
$ sudo echo options nouveau modeset=0 | sudo tee -a /etc/modprobe.d/nouveau-kms.conf
$ sudo update-initramfs -u
```
```sh
sudo reboot now
```
# Kill any possible running Xorg processes
# Note that you might have to run this command multiple times depending on
# how Xorg is configured.
$ sudo killall Xorg
# Check if there is any Xorg process left
# You will have a list of processes running on the GPU, Xorg should not be in
# the list, as shown below.
$ nvidia-smi
# Thu Jun 14 20:21:11 2018
# +-----------------------------------------------------------------------------+
# | NVIDIA-SMI 390.67 Driver Version: 390.67 |
# |-------------------------------+----------------------+----------------------+
# | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
# | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
# |===============================+======================+======================|
# | 0 Tesla K80 On | 00000000:00:1E.0 Off | 0 |
# | N/A 37C P8 31W / 149W | 0MiB / 11441MiB | 0% Default |
# +-------------------------------+----------------------+----------------------+
#
# +-----------------------------------------------------------------------------+
# | Processes: GPU Memory |
# | GPU PID Type Process name Usage |
# |=============================================================================|
# | No running processes found |
# +-----------------------------------------------------------------------------+
```console
# Start the X Server, press Enter to come back to the command line
$ sudo /usr/bin/X :0 &
# Check if Xorg process is running
# You will have a list of processes running on the GPU, Xorg should be in the list.
$ nvidia-smi
# Make the ubuntu use X Server for display
$ export DISPLAY=:0
```
```sh
# For more information on glxgears, see ftp://www.x.org/pub/X11R6.8.1/doc/glxgears.1.html.
$ glxgears
# If Xorg is configured correctly, you should see the following message
# Running synchronized to the vertical refresh. The framerate should be
# approximately the same as the monitor refresh rate.
# 137296 frames in 5.0 seconds = 27459.053 FPS
# 141674 frames in 5.0 seconds = 28334.779 FPS
# 141490 frames in 5.0 seconds = 28297.875 FPS
```
-
In the Unity Editor, load a env project (/env) (you can also use my built enviroment for mac, linux or Windows and go directly to step 6).
-
Open the Build Settings window (menu: File > Build Settings).
-
Select Linux as the Target Platform, and x86_64 as the target architecture (the default x86 currently does not work).
-
Check Headless Mode if you have not setup the X Server. (If you do not use Headless Mode, you have to setup the X Server to enable training.)
-
Click Build to build the Unity environment executable.
-
Upload the executable to your EC2 instance within
Envfolder. -
Change the permissions of the executable.
chmod +x <your_env>.x86_64
-
(Without Headless Mode) Start X Server and use it for display:
# Start the X Server, press Enter to come back to the command line $ sudo /usr/bin/X :0 & # Check if Xorg process is running # You will have a list of processes running on the GPU, Xorg should be in the list. $ nvidia-smi # Make the ubuntu use X Server for display $ export DISPLAY=:0
-
Run Python file
$ cd ~/RoboGym/RoboGym_training $ python RoboGym.py