Self-Aware-Driving-Patate

A project by @dberger @jbarment @ldevelle @llenotre @gilles595

A big thank to Qarnot who supports us through this endeaviour by offering us cloud computing. If you would like to see how we interact with their platforn to launch our calculations, Here's our wrapper repository

Usage:

From base directory (or model save and load is broken):

python3 Archi/train_simulator.py --sim ../../DonkeyCar/DonkeySimLinux/donkey_sim.x86_64 --model 'new_model.h5'

Architecture

Input data
- Interaction with simulator
- From datasets
Preprocessing
- From raw data
Model training
- reward optimisation
- all hyper_parameters given in init()
- followup of metrics (loss / accuracy)
Model evaluation
- Saving the model, with HyperParams
- Evaluate the model

$$ f(x, \mu, \sigma) = \frac{1}{\sigma\sqrt{2\pi}}e ^\frac{-(x -\mu) ^ 2}{2\sigma ^ 2} $$

Utils

Multiprocessing -> Takes complete architecture in hand
Qarnot computing -> HyperParams optim : https://github.com/ezalos/Qarnot_Wrapper
Distance control of WS : https://github.com/ezalos/emails

SimLauncher3000

Source the .env file, import Client, Server and start_server from the package.
use

c = Client()
c.request_simulator()
c.kill_sim()

How to use with SimLauncher3000

In docker

Where we train the agent:

export PS="wesh" ; python3.8 srcs --sim simlaunch3000 --model 'new_model.h5' --agent DDQN

In computer

Where we run the simulator:

cd srcs/simlaunch3000
export PS="wesh" ; export SIM_PATH="/home/ezalos/Downloads/DonkeySimLinux/donkey_sim.x86_64" ; python3.8 test_server.py

ezalos / Self-Aware-Driving-Patate