PICLE

This is a refactored version of the code used for "A Probabilistic Framework for Modular Continual Learning" [1].

Creating a conda environment:

conda create -n PICLE python=3.9
conda activate PICLE
pip install --upgrade pip
pip install -r requirements.txt

Downloading the dataset

The underlying image datasets will be downloaded as needed when running the experiments. The rest of the data is generated on the fly.

Running PICLE on a sequence from BELL

From the project's folder, running the following:

python Experiments/BELL/Experiment.py -sequence S_out -cl_alg PICLE -device cpu -dbg

evaluates PICLE on the S^out sequence of BELL on the cpu, for 1 training epoch per path, and 1 random seed. To evaluate for all 3 random seeds and for more epochs, remove the -dbg argument.

This file can be used to run different baselines on different BELL sequences. To see all options, run:

python Experiments/BELL/Experiment.py -h

Notes:

• The code currently refers to paths as programs, as both specify how an input should be processed.
• If you get an OSError, it might be caused by the result file locker, which safely handles concurrently running algorithms for the same sequence. You can go to Experiments/Interface/Experiment.py and comment out its use on line 44.

Rerefences

[1] Valkov, L., Srivastava, A., Chaudhuri, S. and Sutton, C., 2023. A Probabilistic Framework for Modular Continual Learning. arXiv preprint arXiv:2306.06545.