Autonomous Vehicle Reinforcement Learning using DDPG Algorithm v1.1.4

A CLI tool for running DRL experiments on a simulated Autonomous vehicle platoon (or many).

Install

To install, create a venv and install the requirements file. This program is loosely tested on Python 3.6, 3.7 and 3.8

python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Note: For simulations to render, you will need gym==0.21.0 pyglet==1.5.23

***Note that on windows you will perform venv/Scripts/Activate instead of source venv/bin/activate.

Run the program

To see what the cli can do, enter the following.

python run.py --help

Training

python run.py tr

To gain insight on configurable parameters, use

python run.py tr --help

A training session will create a .outputs folder within the base of the repo. This folder will contain an experiment folder containing the results.

Reporting

After training, you can generate a latex report in a folder. This can be embedded into an overleaf project as a latex subfile if you like.

python run.py lmany

Note that this command only works if the .outputs folder contains an experiment(s).

Figure Generation

If you wish to compare similar experiments, you can use at the accumulator.py file.

python run.py accumr

This will accumulate experiment results and plot the reward averaged across the seeds in svg files for each platoon.

Re-run simulations

You can re-run simulations on existing experiments, by running the below command and passing the file as an argument

python run.py esim

Notes

You can nest --help on any of the above commands if you cant figure out what to do with the cli.