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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Unsupervised Video Object Segmentation for Deep Reinforcement Learning
Authors: Vikash Goel, Jameson Weng, Pascal Poupart
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 15,177 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,591 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Sec. 5 evaluates the approach empirically on 59 Atari games. Finally, Sec. 6 concludes the paper and discusses possible future extensions. |
| Researcher Affiliation | Academia | Vik Goel, Jameson Weng, Pascal Poupart Cheriton School of Computer Science, Waterloo AI Institute, University of Waterloo, Canada Vector Institute, Toronto, Canada EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available at https://github.com/vik-goel/MOREL. |
| Open Datasets | Yes | We showcase the performance of MOREL on all 59 Atari games where we observe a notable improvement in comparison to A2C and PPO for 26 and 25 games respectively, and a worse performance for 3 and 9 games respectively. |
| Dataset Splits | No | The paper mentions training on Atari games and conducting an ablation study but does not explicitly provide details about training, validation, or test dataset splits. |
| Hardware Specification | No | The paper mentions the use of 'Cry SP RIPPLE Facility at the University of Waterloo' but does not provide specific hardware details such as GPU/CPU models or memory amounts used for experiments. |
| Software Dependencies | No | The paper mentions optimizers and algorithms but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We collect 100k frames by following a random policy. Using an Adam optimizer [18] with learning rate 1 × 10−4 and batch size 16, we minimize Ltotal for 250k steps. Following the experimental setup from [24], we train each agent for 10 million timesteps with one timestep for each frame. |