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..
MoCoDA: Model-based Counterfactual Data Augmentation
Authors: Silviu Pitis, Elliot Creager, Ajay Mandlekar, Animesh Garg
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that MOCODA enables RL agents to learn policies that generalize to unseen states and actions. We use MOCODA to train an offline RL agent to solve an out-of-distribution robotics manipulation task on which standard offline RL algorithms fail. |
| Researcher Affiliation | Collaboration | Silviu Pitis 1 Elliot Creager1 Ajay Mandlekar2 Animesh Garg1,2 1University of Toronto and Vector Institute, 2NVIDIA |
| Pseudocode | No | The paper describes the MOCODA framework and its steps (Figure 3), but it does not include a formally labeled "Algorithm" or "Pseudocode" block with structured steps. |
| Open Source Code | Yes | 1Visualizations & code available at https://sites.google.com/view/mocoda-neurips-22/ |
| Open Datasets | No | The paper describes the empirical data used (e.g., 'empirical training data consisting of left-to-right and bottom-to-top trajectories' for 2D Navigation, and 'dataset of 50000 transitions' for Hook Sweep2), but does not provide a specific link, DOI, or formal citation for public access to these datasets themselves. |
| Dataset Splits | Yes | The models are each trained on a empirical dataset of 35000 transitions for up to 600 epochs, which is early stopped using a validation set of 5000 transitions. |
| Hardware Specification | Yes | All our experiments were run on a single NVIDIA A100 GPU. |
| Software Dependencies | No | The paper mentions using the 'Adam optimizer [26]' but does not provide specific version numbers for any programming languages, libraries, or frameworks (e.g., Python, PyTorch, TensorFlow, scikit-learn) used in the implementation. |
| Experiment Setup | Yes | We train all models for 600 epochs using the Adam optimizer [26] with a learning rate of 1e-4 and a batch size of 256. |