A3C run torcs

https://github.com/bn2302/rl_torcs

check： https://github.com/bn2302/rl_torcs/issues

we will replace docker use process!

after run ; Come in for an interview

Reinforcement learning with docker and torcs

Installation

The Torcs reinforcement learning environment is based on an Amazon EC2 g2.2xlarge instance running Ubuntu 16.04.

To setup the environment, the following commands have to be executed from a shell:

git clone https://github.com/bn2302/rl_torcs
cd rl_torc/docker/
sudo su
source root_setup.sh
reboot

After rebooting the instance please run the following commands:

cd rl_torc/docker
source user_setup.sh

The script will install the nvidia drivers, nvidia-docker and an xserver, which is used to connect to the agent via virtualgl.

Next to that the script will build the images for two docker containers:

* Torcs running in a container with virtualgl and turbovnc

* A reinforcement learning environment containing Tensorflow, a modified
  vim and other goodies

Start the docker container

The reinforcement learning docker environment is started using start_rl to reattach the environment the alias attach_rl can be used.

Start the training

The different agents can be trained using the scripts in the src folder called train_X.py. Please not if an agent is prematurely canceled the corresponding torcs container must be stopped using docker stop NAME. To list the running containers please use docker ps -l -a

Monitor the training

To monitor the training process, please connect to the containers, go into the logs directory and start tensorboard using

tensorboard --logdir=a3c_0:'./a3c/train_0/',a3c_1:'./a3c/train_1',a3c_2:'./a3c/train_2',a3c_3:'./a3c/train_3',a3c_4:'./a3c/train_4/',a3c_5:'./a3c/train_5',a3c_6:'./a3c/train_6',a3c_7:'./a3c/train_7',ddpg_1:'./ddpg_1',dddpg_ref:'./ddpg_ref',ddpg_2:'./ddpg_2/'

Tensorboard can be accessed via port 6006 from a browser. When connecting to an AWS instance via ssh, forward the port with -L 6006:localhost:6006. Then tensorboard can be opened in a browser using http://localhost:6006/

Start the testing

Testing is done in the Jupyter notebook test.ipynb . To start the jupyter server run the following command from the main directory

jupyter server --allow-root

Jupyter can be accessed via port 8888 from a browser. When connecting to an AWS instance via ssh, forward the port with -L 6006:localhost8888

References

https://github.com/awjuliani/DeepRL-Agents/blob/master/A3C-Doom.ipynb as the basis for the A3c implementation.

https://github.com/yanpanlau/DDPG-Keras-Torcs as the basis for the DDPG.

https://github.com/plumbee/nvidia-hw-accelerated-box as the basis for the setup scripts.