This repository houses code for the AAAI 2021 paper:
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling
Rohan Chitnis*, Tom Silver*, Joshua Tenenbaum, Leslie Pack Kaelbling, Tomás Lozano-Pérez.
Link to paper: https://arxiv.org/abs/2001.08299
Instructions for running:
- Use Python 3.5 or higher, e.g. with a virtual environment.
- Download Python dependencies:
pip install -r requirements.txt.
- Download the Fast-Forward (FF) planner to any location on your computer. -> Linux: https://fai.cs.uni-saarland.de/hoffmann/ff/FF-v2.3.tgz -> Mac: https://github.com/ronuchit/FF
- From the FF directory you just created, run
maketo build FF, producing the executable
- Create an environment variable "FF_PATH" pointing to this
- Back in the GLIB directory, you can now run
By default, the code runs GLIB-L, GLIB-G, Oracle, and Action babbling (also called "random") on the Blocks domain. If you want another domain or only some of the methods, change
settings.py. Plots will get written out after each seed into an automatically created
results/ folder. Here is an example of the rough shape of plots that should result from running this code out-of-the-box (it may take around 15 minutes to complete):