josefondrej / attainable-utility-preservation

Standing Still Is Not An Option: Alternative Baselines for Attainable Utility Preservation

Standing Still Is Not An Option: Alternative Baselines for Attainable Utility Preservation

A test-bed for the Attainable Utility Preservation (AUP) method for quantifying and penalizing the change an agent has on the world around it. Current AUP approaches however assume the existence of a no-op action in the environment’s action space, which limits AUP to solve tasks where doing nothing for a single time-step is a valuable option. Depending on the environment, this cannot always be guaranteed. We introduce four different baselines that do not build on such actions and therefore extend the concept of AUP to a broader class of environments. We evaluate all introduced variants on different AI safety gridworlds and show that this approach generalizes AUP to a broader range of tasks, with only little performance losses.

This repository further augments this expansion to DeepMind's AI safety gridworlds. For discussion of AUP's potential contributions to long-term AI safety, see here.

Installation

1. Using Python 2.7 as the interpreter, acquire the libraries in requirements.txt.
2. Clone using --recursive to snag the pycolab submodule: git clone --recursive https://github.com/fkabs/attainable-utility-preservation.git.
3. Run python -m experiments.charts or python -m experiments.ablation, tweaking the code to include the desired environments.

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Paper accepted at International IFIP Cross Domain (CD) Conference for Machine Learning & Knowledge Extraction (MAKE) CD-MAKE 2023