Name: Nikhil Sethi
Student Number: 5711428
Date: 11/07/23
This repository contains code for the final project completed for the course Bio-inspired Learning for Aerospace Applications. My aim was to use MADDPG to train 4 drones to flock together in a confined environment.
Video at test time
Learning curve:
The repository was tested with the following versions. Even though they are fairly old, it is highly recommended that you create a virtual environment and use the same versions because they include dependencies from the authors of the main MADDPG repository.
- Ubuntu 18.04
- Python 3.5.10
- Tensorflow 1.8.0
- gym 0.10.5
- matplotlib 3.0.3
- protobuf 3.19.6
git clone [email protected]:nikhil-sethi/marl_flocker.git
cd marl_flocker
git submodule update --init --recursive
cd maddpg/
pip3 install -e .
cd ..
cd multiagent-particle-envs
pip3 install -e .
cd ..
cp scenarios/flocking.py multiagent-particle-envs/scenarios/
cd maddpg/experiments
python train.py --scenario flocking --num-episodes 20000 --save-rate 100 --save-dir <path/to/this/repo>/results/policy
python train.py --restore --display --scenario flocking --load-dir <path/to/this/repo>/results/policy
To reproduce the plots from the paper:
pip install seaborn
python stat.py