COAIC files
training-setup.sh : This will setup a Ubuntu 16.04 server envionment.
coaic-workers.sh : Creates the Workers script to cut and paste into the command line
coaic-universe-creation.sh : Creates the Remotes Environments for Works To Romote into
Other helpful commands:
Google Cloud machine:
n1-highcpu-8
Ubutu 16.04 w/ 20gigs
You can now have preemptive gpus on Google!! :)
(optional) turn on **Preemptibility**
Google Cloud Startup Script
#!/bin/bash
cd /home/ubuntu
git clone https://github.com/gcullie/coaic.git
cd coaic
yes Y | sudo ./training-setup.sh /home/ubuntu
Firewall TCP rules: tcp:5900-5950;tcp:15900-16000
Validate Script Completed
sudo usermod -aG docker $USER
newgrp docker
python
import tensorflow
exit()
Runs a training instance locally
cd ..
cd ubuntu/universe-starter-agent/
sudo python train.py --env-id flashgames.NeonRace-v0 --log-dir ~/NeonRace-v0 -w 2 --visualise
Point to a competition Gym
CUDA_VISIBLE_DEVICES= /usr/bin/python worker.py --log-dir /home/ubuntu/neorace --env-id flashgames.NeonRace-v0 --num-workers 1 --visualise --job-name worker --task 0 --remotes vnc://35.188.180.197:5900+15900
AWS machine
Deep Learning Base AMI (Ubuntu) (ami-f346c289)
p3.2xlarge
(optional) spot instance
Firewall TCP rules: tcp:5900-5950;tcp:15900-16000
cut and paste each row from the script training-setup-aws.sh (the script does not yet work as stand alone)
test with python 'import tensorflow'
git clone https://github.com/openai/universe-starter-agent.git
python3 train.py --env-id flashgames.NeonRace-v0 --log-dir ~/NeonRace-v0 -w 2 --visualise
Tools TMUX CV2 (OpenCV)
Where to go from here
get familiar with the starter agent code.
try running against other games to see what preforms well and where there are limitations.
take a look at https://github.com/openai/baselines/tree/master/baselines for different algorithm ideas.
instead of a digital game, modify the vnc window to manipulate something in read life!
have fun!