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.
A Distributional Framework for Risk-Sensitive End-to-End Planning in Continuous MDPs
Authors: Noah Patton, Jihwan Jeong, Mike Gimelfarb, Scott Sanner9894-9901
AAAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 16,742 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,127 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 | We evaluate and compare RAPTOR on three highly stochastic MDPs, including nonlinear navigation, HVAC control, and linear reservoir control, demonstrating the ability of RAPTOR to manage risk in complex continuous domains according to different notions of risk-sensitive utility. |
| Researcher Affiliation | Collaboration | Department of Mechanical and Industrial Engineering, University of Toronto... Vector Institute, Toronto, Canada. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks clearly labeled as such. |
| Open Source Code | No | The paper does not provide any concrete statements or links regarding the availability of its source code. |
| Open Datasets | No | The paper describes problem domains (Navigation, Reservoir Control, HVAC Control) and how data is modeled or generated within these domains (e.g., normally distributed noise, exponentially-distributed random variable for rainfall), but does not refer to or provide access to any specific, pre-existing publicly available datasets. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages, sample counts, or explicit mention of training, validation, or test sets) needed for data partitioning. |
| Hardware Specification | No | The paper vaguely mentions "on a consumer-grade PC" but does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts). |
| Software Dependencies | No | The paper mentions "Py Torch" and "Adam as the optimizer" but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | No | The paper mentions using "Adam as the optimizer and selected the learning rates according to a grid search" but states that "further experimental details" are in the Appendix, which is not provided, thus specific setup details are missing from the main text. |