Environments from [Toro Icarte et al., 2018] using OpenAI Gym API. This repository currently complements the code for [Furelos-Blanco et al., 2020] and [Furelos-Blanco et al., 2021], whose code is here.
To install the package, you just have to clone the repository and run the following commands:
cd gym-subgoal-automata
pip install -e .
We recommend you to use a virtual environment since the requirements of
this package may affect your current installation. The setup.py file contains the current requirements for the code to run safely.
The learned subgoal automata are exported to .png using Graphviz. You can follow the instructions in the official webpage to install it.
The repository has implementations for the OfficeWorld and WaterWorld environments and different associated success/fail tasks.
You can find the list of all tasks in the file gym_subgoal_automata/__init__.py. The following is an example of how to
instantiate the OfficeWorld environment where the task is "deliver coffee to the office".
import gym
env = gym.make("gym_subgoal_automata:OfficeWorldDeliverCoffee-v0", params={"generation": "random", "environment_seed": 0})
You can use the method env.play() to use the environment with your keyboard using the w, a, s and d keys. In this task you have to observe f (coffee) and then g (office) while avoiding the n (plants/decorations).
We thank the authors of reward machines for open sourcing their code. The code in this repository is heavily based on theirs.
- Toro Icarte, R.; Klassen, T. Q.; Valenzano, R. A.; and McIlraith, S. A. 2018. Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning. Proceedings of the 35th International Conference on Machine Learning.
- Furelos-Blanco, D.; Law, M.; Russo, A.; Broda, K.; and Jonsson, A. 2020. Induction of Subgoal Automata for Reinforcement Learning. Proceedings of the 34th AAAI Conference on Artificial Intelligence.
- Furelos-Blanco, D.; Law, M.; Jonsson, A.; Broda, K.; and Russo, A. 2021. Induction and Exploitation of Subgoal Automata for Reinforcement Learning. J. Artif. Intell. Res., 70, 1031-1116.