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..
SeRO: Self-Supervised Reinforcement Learning for Recovery from Out-of-Distribution Situations
Authors: Chan Kim, Jaekyung Cho, Christophe Bobda, Seung-Woo Seo, Seong-Woo Kim
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our in-depth experimental results demonstrate that our method substantially improves the agent s ability to recover from OOD situations in terms of sample efficiency and restoration of the performance for the original tasks. We conducted experiments on four Open AI gym s Mu Jo Co environments to answer the above questions. |
| Researcher Affiliation | Academia | Chan Kim1 , Jaekyung Cho1 , Christophe Bobda2 , Seung-Woo Seo1 and Seong-Woo Kim1 1Seoul National University 2University of Florida EMAIL, EMAIL |
| Pseudocode | No | A detailed explanation of the overall retraining procedure can be found in the supplementary material. |
| Open Source Code | Yes | Code and supplementary materials are available at https://github.com/SNUChan Kim/Se RO. |
| Open Datasets | Yes | We used Half Cheetah-v2, Hopper-v2, Walker2D-v2, and Ant-v2 from the gym s Mu Jo Co environments [Brockman et al., 2016]. |
| Dataset Splits | No | The paper describes training and retraining phases in simulation environments but does not provide specific dataset split information (e.g., percentages or sample counts) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Open AI gym s Mu Jo Co environments' and 'SAC' and 'Python' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We first trained the agents in the training environments for 1 million steps using SAC |