Measuring Goal-Directedness
Authors: Matt MacDermott, James Fox, Francesco Belardinelli, Tom Everitt
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We prove that MEG satisfies several desiderata and demonstrate our algorithms with small-scale experiments 1. and We carried out two experiments5 to measure known-utility MEG with respect to the environment reward function and unknown-utility MEG with respect to a hypothesis class of utility functions. |
| Researcher Affiliation | Collaboration | Matt Mac Dermott Imperial College London James Fox University of Oxford London Initiative for Safe AI Francesco Belardinelli Imperial College London Tom Everitt Google DeepMind |
| Pseudocode | Yes | Algorithm 1 Known-utility MEG in MDPs and Algorithm 2 Unknown-utility MEG in MDPs |
| Open Source Code | Yes | 5Code available at https://github.com/mattmacdermott1/measuring-goal-directedness |
| Open Datasets | Yes | Our experiments measured MEG for various policies in the Cliff World environment from the seals suite [Gleave et al., 2020]. |
| Dataset Splits | No | The paper does not provide specific dataset split information for training, validation, or testing. |
| Hardware Specification | Yes | Hardware model: LENOVO20N2000RUK Processor: Intel(R) Core(TM) i7-8665U CPU @ 1.90GHz, 2112 Mhz, 4 Core(s), 8 Logical Processor(s) Memory: 24.0 GB |
| Software Dependencies | No | The paper mentions using ‘SEALS library’ and ‘imitation library’ but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We used an MLP with a single hidden layer of size 256 to define a utility function over states. and considering ε-greedy policies for ε in the range 0.1 to 0.9. |