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Code for the paper "AlwaysSafe: Reinforcement Learning Without Safety Constraint Violations During Training"

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AlwaysSafe

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.

[Details]

modules

  • 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.

lp solver

By default, the code uses gurobipy if found, otherwise it uses cvxpy.

usage

  1. install dependencies
    pipenv install
    
  2. run tests
    pipenv run python -m unittest
  3. reproduce the experiments
    pipenv run python -m scripts.simple
    pipenv run python -m scripts.factored
    pipenv run python -m scripts.cliff_walking

citing

@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},
}

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