Code for the paper "AlwaysSafe: Reinforcement Learning Without Safety Constraint Violations During Training" — Thiago D. Simão, Nils Jansen and Matthijs T. J. Spaan, published at AAMAS 2021.
agents: model based RL agents that interact with the environment.planners: the planners used by the RL agents to compute the policy in each episode.scripts: each file is related to one of the experiments from the paper.tests: mostly unittest scripts.util: contains common scripts to train an RL agent and evaluate a policy.
By default, the code uses gurobipy if found, otherwise it uses cvxpy.
- install dependencies
pipenv install - run tests
pipenv run python -m unittest
- reproduce the experiments
pipenv run python -m scripts.simple pipenv run python -m scripts.factored pipenv run python -m scripts.cliff_walking
@inproceedings{Simao2021alwayssafe,
author = {Sim{\~a}o, Thiago D. and Jansen, Nils and Spaan, Matthijs T. J.},
title = {AlwaysSafe: Reinforcement Learning Without Safety Constraint Violations During Training},
year = {2021},
booktitle = {Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS)},
publisher = {IFAAMAS},
pages = {1226–1235},
}