Experiments for "ENROS: Entity-based Reinforcement Learning with Open-world Scene Understanding"

This repository contains all the code and configs used to produce the results for ENROS.

Setup

Install the requirements with pip install -r requirements.txt, make sure that the Cuda version for PyG matches your installed version.

Then run pip install <-e> ..

Each experiment is defined by a config file, for the tuned examples see the configs directory.

Create a log directory and place a config file in it named conf.yaml. Then run

python scripts/train.py --logdir <logdir>

See example configs in the configs directory.

Note, that Atari was only tuned on Pong, but it can be run on any games supported by ALE.

For videos of the trained agents performing see this playlist.

The proxy task use scripts/train-gdino.py with the same config file format as the previous experiments.

The experiences are generated with scripts/run-env.py, and passed to the script:

python scripts/train-gdino.py --cfg <configfile> --data <datafile>