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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

Architecture Overview

  1. Input data

    • Interaction with simulator
    • From datasets

  2. Preprocessing

    • From raw data

  3. Model training

    • reward optimisation
    • all hyper_parameters given in init()
    • followup of metrics (loss / accuracy)

  4. 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

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="/Users/deyaberger/projects/DonkeySimMac/donkey_sim.app/Contents/MacOS/donkey_sim" ; python3.8 test_server.py

Data

Three elements can be saved locally or on S3:

Model weights, config info, simulator cache

To save and load everything from S3, please activate this variable in config:

config_Datasets.S3_connection = True


The config information will be automatically saved if Model weights saving is activated (saved in the same folder as model weights)

1 - Model Weights:


1a) Loading a model:

function "_load_model" in Agent class.

config_Datasets.load_model = True
config_Datasets.model_to_load = <the_model_name>

<the_model_name> is just the file name, in S3 or locally. The complete path depends on:
config_Datasets.local_model_folder if it is local
config_S3.model_folder if it is with S3 Once those 2 folders names are correct, you won't have to change them all the time

1b) Saving a model:

function "save_modelo" in Agent class. Please change accordingly the following config variable:

config_Datasets.saving_frequency

(equals to 0 if you want to turn off the saving)
you won't need to change anything in config, just make sure that the following variables suits you:
config_Datasets.model_to_save_name config_Datasets.local_model_folder (folder must be at the root of the project) config_S3.model_folder (path after bucket_name)

2) Simulator Cache:

It is all the infos that are returned by the function "simulator.env.step(action)", aka: state, action, new_state, reward, done, infos

2a) Saving datapoints:

Function "add_simcache_point" in Agent class You can change the following variable to your conveniance:

config_Datasets.size_SimCache = 300

Make sure this variable equals to True:

config_Datasets.save_SimCache = True

300 is the number of datapoints contained in one simcache file. The higher the number, the heavier the file.
Make sure the path variables are correct so that it saves in the right path: config_Datasets.sim_infos_name config_Datasets.local_sim_folder for local saving (must be in the root of the project) config_S3.simulator_folder for S3 saving (path after the bucket name)

2b) Loading datapoints:

If you want to use simcache in your training instead of the simulator itself, please look at the function "train_agent_from_SimCache" in NeuralPlayer
You can either pick on file or an entire folder (S3 or locally) If it is just one file, then do the following:

config_Datasets.sim_from_folder = False
config_Datasets.sim_to_load = <your_file_name>

If you want to pick all files from one folder:

config_Datasets.sim_from_folder = True
config_Datasets.local_sim_folder = <your_folder>

or with S3:

config_Datasets.sim_from_folder = True
config_S3.simulator_folder = <S3_folder>