Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Invariant Causal Prediction for Block MDPs
Authors: Amy Zhang, Clare Lyle, Shagun Sodhani, Angelos Filos, Marta Kwiatkowska, Joelle Pineau, Yarin Gal, Doina Precup
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We give empirical evidence that our methods work in both linear and nonlinear settings, attaining improved generalization over singleand multi-task baselines. |
| Researcher Affiliation | Collaboration | 1Mc Gill University 2Mila 3Facebook AI Research 4University of Oxford 5Deepmind. |
| Pseudocode | Yes | Algorithm 1 Linear MISA Algorithm 2 Nonlinear Model-irrelevance State Abstraction (MISA) Learning |
| Open Source Code | Yes | Code is available at https://github.com/facebookresearch/ icp-block-mdp. |
| Open Datasets | Yes | We randomly initialize the background color of two train environments from Deepmind Control (Tassa et al., 2018) from range [0, 255]. |
| Dataset Splits | No | The paper describes training and test environments but does not explicitly mention a separate validation set or how data was split for validation purposes. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU models, CPU models, or memory specifications. |
| Software Dependencies | No | The paper does not specify the versions of any software dependencies (e.g., libraries, frameworks) used in the experiments. |
| Experiment Setup | Yes | Implementation details found in Appendix C.1. Implementation details and more information about Soft Actor Critic can be found in Appendix C.2. Additional plots with the hyperparameter sweep done to ο¬nd a good penalty weight for IRM can also be found Appendix C.3. |