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.
Policy Gradient for Rectangular Robust Markov Decision Processes
Authors: Navdeep Kumar, Esther Derman, Matthieu Geist, Kfir Y. Levy, Shie Mannor
NeurIPS 2023 | Venue PDF | LLM Run Details | Input Tokens: 27,811 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,696 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 | Experiments show that our RPG speeds up state-of-the-art robust PG updates by 2 orders of magnitude. and In the following experiments, we randomly generate nominal models for arbitrary state-action space sizes. Each experiment was averaged over 100 runs. |
| Researcher Affiliation | Collaboration | Navdeep Kumar Technion Esther Derman MILA, Université de Montréal Matthieu Geist Goodle Deepmind Kfir Levy Technion Shie Mannor Technion, NVIDIA Research |
| Pseudocode | Yes | Algorithm 1 RPG |
| Open Source Code | Yes | All codes and results are available at https://github.com/navdtech/rpg. |
| Open Datasets | No | In the following experiments, we randomly generate nominal models for arbitrary state-action space sizes. |
| Dataset Splits | No | The paper does not provide specific dataset split information (train/validation/test) as it uses randomly generated models rather than a pre-existing dataset with defined splits. |
| Hardware Specification | Yes | Hardware Experiments are done on the machine with the following configuration: Intel(R) Core(TM) i7-6700 CPU @3.40GHZ, size:3598MHz, capacity 4GHz, width 64 bits, memory size 64 Gi B. |
| Software Dependencies | No | All the experiments were done in Python using numpy, matplotlib. |
| Experiment Setup | Yes | Discount factor γ = 0.9, reward noise radius αs,a, αs = 0.1, transition noise kernel βs,a, βs = 0.01 |