Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning

This repository contains the code for our paper Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning, which was accepted at ICML 2020. The FQI implementation is derived from TRLIB.

Requirements

Python 3
numpy
scikit-learn
joblib
matplotlib
gym

Example

To run PFQI for different persistences in the Cartpole environment:

python3 scripts/run_cartpole.py

The results will be stored in a json file. To plot the results:

python3 plotters/multi_perf_plotter4x4.py plotters/example.json

Citing

@incollection{metelli2020control,
    author = "Metelli, Alberto Maria and Mazzolini, Flavio and Bisi, Lorenzo and Sabbioni, Luca and Restelli, Marcello",
    booktitle = "Proceedings of the 37th International Conference on Machine Learning, Online, PMLR 119, 2020",
    pages = "4102--4113",
    title = "Control Frequency Adaptation via Action Persistence in Batch Reinforcement Learning",
    year = "2020",
}