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purl

Pathfinding Using Reinforcement Learning

Algorithms

MDP (using the FullyObsWrapper)

  • Q-table
  • Q-network

POMDP

  • PPO
  • DQN (with the Double DQN extension) - work in progress, not currently working as intended
  • DRQN - work in progress, not currently working as intended

Getting Started

Prerequisites

python + pip, version 3.6 or greater

Installing

Note: It's recommended that you install the Python dependencies in a virtual environment. virtualenv and virtualenvwrapper:

pip install virtualenv
pip install virtualenvwrapper

Note: To get matplotlib to work on a Mac with a virtual environment you have to use venv instead

Setting up a virtual environment

mkvirtualenv purl

or with venv:

python -m venv purl-venv

Installing Python dependencies

First, switch to the virtual environment:

workon purl

or with venv:

source purl-venv/bin/activate

Note: You can set up an alias in your shell to make the virtual environment more accessible, e.g alias actpurl='source /path/to/purl/purl-venv/bin/activate'

Then, install the dependencies by running:

pip install -r requirements.txt

or if you have pip-sync installed:

pip-sync

Running purl

There are two main subcommands to PURL

train

To train a model, run:

./purl train

For example, to train a model using the PPO algorithm on the MiniGrid-LavaCrossingS9N1-v0 environment, use the following arguments:

./purl train --algorithm ppo --environment MiniGrid-LavaCrossingS9N1-v0

visualize

To visualize a model, run:

./purl vizualize

Development

Updating dependencies

Python dependencies are managed by pip-compile

To add a new package, simply add it to the list in requirements.in.

You then update the requirements.txt-file by running

pip-compile --output-file requirements.txt requirements.in

Built With

  • gym-minigrid - Minimalistic gridworld environment for OpenAI Gym
  • PyTorch - Tensors and Dynamic neural networks in Python

Authors

  • Anne Engström
  • Joel Lidin
  • Gustav Molander
  • Olle Månsson
  • Noa Onoszko
  • Hugo Ölund

License

This project is licensed under the MIT License - see the LICENSE file for details