Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Risk-Averse Model Uncertainty for Distributionally Robust Safe Reinforcement Learning
Authors: James Queeney, Mouhacine Benosman
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 24,885 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,802 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 | In experiments on continuous control tasks with safety constraints, we demonstrate that our framework produces robust performance and safety at deployment time across a range of perturbed test environments. |
| Researcher Affiliation | Collaboration | James Queeney Division of Systems Engineering Boston University EMAIL Mouhacine Benosman Mitsubishi Electric Research Laboratories EMAIL |
| Pseudocode | Yes | Algorithm 1 Risk-Averse Model Uncertainty for Safe RL |
| Open Source Code | Yes | Code is publicly available at https://github.com/jqueeney/robust-safe-rl. |
| Open Datasets | Yes | we conduct experiments on 5 continuous control tasks with safety constraints from the Real-World RL Suite [18, 19] |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, and testing. |
| Hardware Specification | Yes | All experiments were run on a Linux cluster with 2.9 GHz Intel Gold processors and NVIDIA A40 and A100 GPUs. |
| Software Dependencies | No | The paper mentions software components like 'ELU activations' and 'multivariate Gaussian policy' but does not specify their version numbers or the versions of underlying deep learning frameworks or libraries. |
| Experiment Setup | Yes | Table 5: Network architectures and algorithm hyperparameters used in experiments |